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ISBN 978-952-62-1726-0 (Paperback)ISBN 978-952-62-1727-7 (PDF)ISSN 0355-3213 (Print)ISSN 1796-2226 (Online)

U N I V E R S I TAT I S O U L U E N S I SACTAC

TECHNICA

U N I V E R S I TAT I S O U L U E N S I SACTAC

TECHNICA

OULU 2017

C 633

Harri Viittala

SELECTED METHODS FOR WBAN COMMUNICATIONS — FM-UWB AND SMARTBAN PHY

UNIVERSITY OF OULU GRADUATE SCHOOL;UNIVERSITY OF OULU,FACULTY OF INFORMATION TECHNOLOGY AND ELECTRICAL ENGINEERING,CENTRE FOR WIRELESS COMMUNICATIONS;INFOTECH OULU

C 633

AC

TAH

arri Viittala

C633etukansi.kesken.fm Page 1 Monday, October 30, 2017 8:49 AM

ACTA UNIVERS ITAT I S OULUENS I SC Te c h n i c a 6 3 3

HARRI VIITTALA

SELECTED METHODS FOR WBAN COMMUNICATIONS — FM-UWB AND SMARTBAN PHY

Academic dissertation to be presented with the assent ofthe Doctoral Training Committee of Technology andNatural Sciences of the University of Oulu for publicdefence in the OP auditorium (L10), Linnanmaa, on 15December 2017, at 12 noon

UNIVERSITY OF OULU, OULU 2017

Copyright © 2017Acta Univ. Oul. C 633, 2017

Supervised byProfessor Jari IinattiDocent Matti Hämäläinen

Reviewed byProfessor Ilangko BalasinghamDoctor Huan-Bang Li

ISBN 978-952-62-1726-0 (Paperback)ISBN 978-952-62-1727-7 (PDF)

ISSN 0355-3213 (Printed)ISSN 1796-2226 (Online)

Cover DesignRaimo Ahonen

JUVENES PRINTTAMPERE 2017

OpponentAssistant Professor Luca De Nardis

Viittala, Harri, Selected methods for WBAN communications — FM-UWB andSmartBAN PHY. University of Oulu Graduate School; University of Oulu, Faculty of Information Technologyand Electrical Engineering; Centre for Wireless Communications; Infotech OuluActa Univ. Oul. C 633, 2017University of Oulu, P.O. Box 8000, FI-90014 University of Oulu, Finland

Abstract

The value of wearable market is booming, especially in the healthcare application segment. Thissegment is driven by an increasing need for regular monitoring and early diagnosis of patients withgrowing prevalence of chronic diseases.

Wireless communications worn in the close proximity of the body, the variety of applications,and their requirements set design considerations and challenges. In addition to the technicalrequirements, coexistence with adjacent wireless body area networks (WBANs) and otherwireless systems need to be taken into account. A WBAN system needs to be highly reliable, lowpower, fast, and interference-immune.

This thesis studies the performance of two different PHY layer implementations in interferedfading channels. The systems are the frequency modulated ultra wideband (FM-UWB), defined inthe IEEE 802.15.6 standard, and narrowband SmartBAN physical layer. The performance of thesystems was analyzed by using software simulators developed in Matlab. The author developedthe SmartBAN simulator for the ETSI Technical Committee (TC) SmartBAN to study theperformance of the new SmartBAN system. This is the first physical layer performance study ofthe SmartBAN system. In addition, the open literature does not offer similar results on the FM-UWB as presented in this thesis.

Based on the results, it can be concluded that the FM-UWB is performing well in situationswhere high reliability and high interference tolerance is needed. In addition, the simplicity of theFM-UWB transceiver makes it more suitable than the direct sequence UWB (DS-UWB) forapplications with data rates of hundreds of kbps. SmartBAN has the best performance in caseswhere more relaxed requirements for reliability and interference tolerance can be applied.Nevertheless, it became obvious that both systems need proper coexistence and interferencemitigation mechanisms to ensure reliability in all scenarios.

Keywords: IEEE 802.15.6, interference, narrowband, physical layer, ultra wideband,wireless body area network

Viittala, Harri, Menetelmiä langattoman kehoverkon tiedonsiirtoon – FM-UWB jaSmartBAN PHY. Oulun yliopiston tutkijakoulu; Oulun yliopisto, Tieto- ja sähkötekniikan tiedekunta; Centre forWireless Communications; Infotech OuluActa Univ. Oul. C 633, 2017Oulun yliopisto, PL 8000, 90014 Oulun yliopisto

Tiivistelmä

Puettavien laitteiden markkina-arvo on voimakkaassa kasvussa erityisesti terveydenhuollonsovellusalueella. Tämän sovellusalueen kiihdyttimenä toimii yhä suurempi tarve potilaiden kun-non jatkuvalle tarkkailulle sekä kroonisille taudeille alttiimpien potilaiden varhaiselle diagno-soinnille.

Langattoman kehoverkon (WBAN) suunnittelun suurimpia haasteita ovat langaton tiedonsiir-to kehon läheisyydessä, erilaiset sovellustyypit sekä niiden vaatimukset. Teknisten vaatimustenlisäksi on myös huomioitava rinnakkaiset kehoverkot sekä muut langattomat järjestelmät. Keho-verkkojärjestelmän on oltava todella luotettava, matalatehoinen, nopea ja häiriösietoinen.

Väitöskirjassa tutkitaan kahta kehoverkon fyysisen kerroksen toteutusta häipyvissä ja häiri-tyissä kanavissa. Nämä toteutukset ovat IEEE 802.15.6 -standardissa määritelty taajuusmoduloi-tu ultralaajakaista (FM-UWB) sekä kapeakaistainen SmartBAN. Järjestelmien suorituskykyäanalysoitiin Matlab-ohjelmistosimulaattoreiden avulla. Työssä kehitettiin SmartBAN-simulaat-tori ETSI Technical Committee (TC) SmartBAN -työryhmälle järjestelmän suorityskykytutki-mukseen. Tässä työssä esitetään SmartBAN-järjestelmän fyysisen kerroksen suorituskykytulok-set, jotka ovat ensimmäiset laatuaan. Lisäksi kirjallisuudesta ei löydy vastaavia tuloksia FM-UWB:n osalta, kuten tässä työssä on esitetty.

Tuloksien pohjalta voidaan päätellä, että FM-UWB suoriutuu hyvin tilanteissa, joissa vaadi-taan suurta luotettavuutta sekä suurta häiriönsietokykyä. Lisäksi yksinkertainen lähetin-vastaan-otinrakenne tekee siitä kiinnostavamman vaihtoehdon kuin suorahajotettu UWB (DS-UWB)sovelluksille, jotka vaativat satojen kbps:n tiedonsiirtonopeutta. SmartBAN toimii hyvin tilan-teissa, joissa näistä vaatimuksista voidaan hieman joustaa. Kuitenkin on selvää, että molemmatjärjestelmät tarvitsevat sopivan rinnakkais- ja häiriönvaimennustekniikan taatakseen luotettavuu-den kaikissa tapauksissa.

Asiasanat: fyysinen kerros, häiriö, IEEE 802.15.6, kapeakaistainen, langatonkehoverkko, ultralaajakaistainen

To Tiina, Eino, and Väinö

Preface

The research work for this thesis was carried out during the years 2007–2010 and2016–2017 at the Centre for Wireless Communications (CWC) at the University ofOulu. Between the thesis research, the author engaged with industry funded projects.Although these projects did not directly relate to the thesis research, but they providedvaluable insight into the industry. The project funding was provided by the Academyof Finland via the DWHN (Dependable Wireless Healthcare Networks) project. TheEuropean Telecommunications Standards Institute (ETSI) provided funding to SpecialistTask Force 511 (STF511). In addition, the CWC funded its internal WiMeC (WirelessMedical Communications) project. All the funding partners are highly appreciated formaking this work possible.

The work would not have been possible without the effort and support of thefollowing people whom I would like to thank personally. I would like to express mydeepest gratitude to my supervisors Professor Jari Iinatti and Docent Matti Hämäläinen.They both have been guiding me since I started my Master’s thesis at the CWC. Iwould also like to thank you for all the opportunities and confidence you gave to me. Iwould also like to thank the pre-examiners of this thesis, Prof. Ilangko Balasingham,Department of Electronics & Telecommunications, Norwegian University of Science &Technology (NTNU), Trondheim, Norway, and Dr.Eng. Huan-Bang Li, Chief SeniorResearcher, National Institute of Information and Communications Technology (NICT),Japan.

I could not have finished the studies without my follow-up group, so my sincerethanks go to Docent Kari Kärkkäinen and Dr.Sc. Pekka Pirinen. I have also receivedinvaluable help from late John F.M. Gerrits, who provided me with technical guidance indeveloping the FM-UWB simulator. I would also like to thank Dr.Sc. Andrew Fort andDr.Sc. Attaphongse Taparugssanagorn for sharing their channel models with me. I wasalso privileged to work with Prof. Lorenzo Mucchi in the ETSI Specialist Task Force 511.There were also numerous meetings with the Wireless Medical Communications projectgroup to share ideas. I would like to thank the group members Heikki Karvonen, VilleNiemelä, Tommi Tuovinen, Timo Kumpuniemi, Tuomas Paso, Juha Petäjäjärvi MariellaSäestöniemi, and Marcos Katz for your support. I am also very grateful for the currentand former administrative personnel of the CWC, Jari Sillanpää, Kirsi Ojutkangas,

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Eija Pajunen, Hanna Saarela, Juha-Pekka Mäkelä, Timo Äikäs, Sari Luukkonen, andElina Komminaho. The coffee break gang including Ari Isola, Tero Suutari, and MarkoHärkönen were a fresh breath of air during workdays. There are two colleagues whodeserve special thanks. Working and spending time with Jani Saloranta and SimoneSoderi taught me a lot, both professionally and personally. Ulla Tuominen Foundationand Seppo Säynäjäkankaan Tiedesäätiö awarded personal grants for my doctoral studies.The financial support is highly appreciated.

Besides the work, my warmest gratitude goes to my childhood friends, Matti, Arto,Tuomas, Kimmo, Pekka, and Olli. Even though, we meet only once a year, it is alwayslike coming home. Kickboxing Team and its coach Leif Stenius have led me to the roadto self-knowledge. They say that the ring is the loneliest place in the world. But it isthe place where you can explore yourself. Thank you for supporting and believing inme. I want to express my gratitude to my parents Ari and Tarja and my sister Heidi forencouragement and support through out my life and studies. In addition, I wish to thankmy parents-in-law Ari and Seija for their support.

Finally, my warmest and deepest thanks go to my family Tiina, Eino, and Väinö fortheir love and support. You are my everything. This thesis is dedicated to you.

Oulu, October, 2017 Harri Viittala

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List of symbols and abbreviations

()∗ Complex conjugate∗ Convolutional operationchol() Cholesky factorizationE() Expected value

A Amplitudeal Path amplitude for the l-th pathan Complex gain of the n-th multipath componentb Channel binb[k] k-th data bitBDEMOD Useful bandwidth of the FM demodulatorBRF RF bandwidthBSUB Subcarrier bandwidthBT 3-dB bandwidth-symbol time productBW BandwidthC Covariance matrixcp Spreading code sequenced Distanced[k] k-th modulated bitE Electric field strengthe[k] Encoded bitsEb Bit energyEb/N0 Bit energy to noise power spectral density ratioEr Number of echoes in a regionf Frequencyfc Carrier/center frequencyfl Lower frequencyfm Frequency of a modulating signalfu Upper frequencyg(t) Gaussian pulse shapeGb,dB Gain of the bin b expressed in dB

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GdB(τl) Magnitude of the cluster arriving at excess delay τl expressed in dBGσ Standard deviation around the average trendH Toeplitz matrix derived from hp(t)

h(t) Channel impulse responsehcgn(t) Impulse response of the CGN filterHcgn( f ) Frequency response of the CGN filterHp( f ) Frequency maskhp(t) Impulse response corresponding with the frequency mask Hp( f )

Hrc( f ) Frequency response of the raised cosine waveform filterhrc(t) Impulse response of the raised cosine waveform filteri Data bit i

i(t) InterferenceK Number of the arrival clustersk Message lengthK0 Fit with measurement data for the K-factor for low path lossL Number of the arrival pathsL Average number of L

LD Number of time slots in an inter-beacon intervalLslot Number of slots in a time slotM Mean value vector for each channel binm Unit mean, unit variance normally distributed random variablem(t) Modulating signalm0 Average decay ratemK Slope of the linear correlation between the path loss and K-factorN Number of samples per Tm

n Codeword lengthn(t) Additive white Gaussian noise(n,k) BCH code rateNb Number of channel binsNc Length of the code sequencenc Channel numberNCM Number of time slots in a control and management periodNr Set of resolvable multipath componentsnK Gaussian random variable with zero mean and unit variancenP Gaussian random variable with the zero mean and unit variance

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NS Number of time slots in a schedule periodO OverdriveP0 Average loss close to the antennap(t) Pulse shape meeting Hp(t)

PdB(d) Path loss at the distance d

P1 Average attenuation of componentsPGDS Processing gain of DS-UWBPGFM Processing gain of FM-UWBr[k] Received bitsr(t) Received signalRb Bit rateS Normal distribution with zero-mean and standard deviation of σS

s Shape parameters(t) Transmitted signalsDS(t) DS-UWB signalsFM(t) FM-UWB signalSIR Signal-to-interference power ratioSNRRF RF signal-to-noise power ratioSNRSUB Subcarrier SNRT Symbol timet TimeTb Bit durationTC Interval between control channel beaconsTc Chip durationTD Interval between data channel beaconste Error correction capabilitytl Path arrival time for the l-th pathtl,k Path arrival time for the l-th path relative to the k-th clusterTm Pulse durationTmin Minimum duration of slotTS Duration of a time slotUi Decision variable for rake combiningVFM,out Output voltage of the FM demodulatorwi(t) Transmitted pulse waveform for the data bit i

X Shadowing term

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x Vector of Nb uncorrelated, zero mean, unit variance normal variablesxG(t) Gaussian monocyclex(n)G (t) n-th order derivative of the Gaussian monocyclexp(t) Pulse waveform

α Roll-off factorαl,k Multipath gain coefficient for the l-th path relative to the k-th clusterαW Shape parameter of the Weibull distributionβ Modulation indexβl,k Fading associated with the l-th path of the k-th clusterβW Scale parameter of the Weibull distributionΓ Cluster decay factorγr Ray decay factorγτk Exponential decaying slope for multipath components within a clusterγ0 Rician factorΓ1 Cluster decay rate of the first groupΓ2 Cluster decay rate of the second group∆ f Frequency deviationδ (t) Dirac delta functionε Specific absorption rateη Scale parameterθ Threshold parameterΛ Cluster arrival rateλ Ray arrival rateµ Location parameterν Attenuation factorξ Spectral efficiency parameterξk Fading associated with the k-th clusterρ Mass density of tissueσ Standard deviation/Conductivity of tissueσK Log–normal varianceσ2

n Noise varianceσP Log–normal varianceσX Standard deviation of lognormal shadowing term for total multipath

realization

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σ1 Standard deviation of cluster lognormal fading termσ2 Standard deviation of ray lognormal fading termτ Delayτbp Breakpoint delayτk Path arrival time within a clusterτl Cluster arrival timeτn Delay of the n-th multipath componentτRMS RMS delayφl Random phase for the l-th path modeled by a uniform distribution over

[0,2π)

ϕ0 Arbitrary, time-independent constant phaseψ(t) Time-limited pulseω Angular frequency

AFA Adaptive frequency agilityAM Amplitude modulationARake All-rakeAWGN Additive white Gaussian noiseBAN Body area networkBCH Bose-Chaudhuri-HocquenghemBER Bit error rateBPAM Binary pulse amplitude modulationBW BandwidthCAP Contention access phaseC-Beacon Control channel beaconCCH Control channelCGN Colored Gaussian noiseCM Channel modelCP-BFSK Continuous phase binary FSKCSMA/CA Carrier sense multiple access with collision avoidanceDAA Detect and avoidD-Beacon Data channel beaconDBPSK Differential binary phase-shift keyingDC Duty cycleDCH Data channel

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DQPSK Differential quadrature phase-shift keyingDSSS Direct sequence spread spectrumDS-UWB Direct sequence ultra widebandEAP Exclusive access phaseECC Electronic Communication CommitteeECG ElectrocardiographyEGC Equal gain combiningEIRP Effective isotropic radiated powerEMG ElectromyographyERC European Radio Communications CommitteeERP Equivalent radiated powerETSI European Telecommunications Standards InstituteEU European UnionFB Front-to-backFCC Federal Communications CommitteeFDMA Frequency division multiple accessFER Frame error rateFF Front-to-frontFH Frequency hoppingFHSS Frequency hopping spread spectrumFM Frequency modulationFM-UWB Frequency modulated ultra widebandFSK Frequency shift keyingGEV Generalized extreme valueGFSK Gaussian frequency shift keyingHARQ Hybrid automatic repeat requestHBC Human body communicationsIBI Inter-beacon intervalICU Intensive care unitIEEE Institute of Electrical and Electronics EngineersIoT Internet–of–thingsIP Internet protocolIR-UWB Impulse radio UWBISM Industrial, scientific and medicalLBT Listen before talk

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LDC Low duty cycleLE Low energyLOS Line–of–sightMAC Medium access controlMAP Managed access phaseMedRadio Medical Device Radiocommunications ServiceMLSD Maximum likelihood sequence detectorMPC Multipath componentMPDU MAC protocol data unitMRC Maximum ratio combiningOFDM Orthogonal frequency-division multiplexingOOK On-off keyingOR Operating roomPHR Physical layer headerPHY Physical layerPLCP PHY layer convergence protocolPPDU PHY layer protocol data unitPRake Partial-rakePSD Power spectral densityPSDU PHY layer service data unitQoS Quality of serviceRAP Random access phaseRF Radio frequencyRMS Root mean squareSAR Specific absorption rateSHR Synchronization headerSIR Signal-to-interference power ratioSLC Square-law combiningSmartBAN Smart body area networkSNR Signal-to-noise power ratioSoC System on chipSRake Selective-rakeSRD Short range deviceSRRC Square root raised cosineSTF511 Specialist task force 511

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TC Technical CommitteeTDMA Time division multiple accessULP-AMI Ultra low power active medical implantUWB Ultra widebandWBAN Wireless body area networkWGN White Gaussian noiseWI Work itemWSN Wireless sensor networkXR X-ray examination room

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List of original publications

This thesis is based on the following original papers, which are referred to as follows bytheir Roman numerals (I–VI).

I Viittala H, Hämäläinen M & Iinatti J (2008) Suitability Study of DS-UWB and UWB-FMfor Medical Applications. In: The 11th International Symposium on Wireless PersonalMultimedia Communications, pp. 1–5.

II Viittala H, Nahar BN, Hämäläinen M & Iinatti J (2010) Medical Applications AdaptingUltra Wideband: A System Study. International Journal of Ultra Wideband Communicationsand Systems 1(4): 237–247, DOI: 10.1504/IJUWBCS.2010.034305.

III Viittala H, Hämäläinen M & Iinatti J (2010) Impact of Difference in WBAN ChannelModels on UWB System Performance. In: IEEE 11th International Symposium on SpreadSpectrum Techniques and Applications, pp. 175–180,DOI: 10.1109/ISSSTA.2010.5651033.

IV Viittala H, Hämäläinen M & Iinatti J (2017) Link-level Performance of FM-UWB inthe Interfered IEEE 802.15.6 Channel. In: 12th International Conference on Body AreaNetworks, pp. 1–4.

V Viittala H, Mucchi L, Hämäläinen M & Paso T (2017) ETSI SmartBAN System Performanceand Coexistence Verification for Healthcare. IEEE Access, Vol 5: 8175–8182, DOI:10.1109/ACCESS.2017.2697502.

VI Viittala H, Mucchi L & Hämäläinen M (2017) Performance of the ETSI SmartBAN Systemin the Interfered IEEE 802.15.6 Channel. In: 11th International Symposium on Medical In-formation and Communication Technology, pp. 1–4, DOI: 10.1109/ISMICT.2017.7891757.

Professor Jari Iinatti and Docent Matti Hämäläinen acted as the supervisors and providedguidance and scientific support for all the Papers I–VI. Late John F.M. Gerrits, CentreSuisse d’Electronique et de Microtechnique S.A (CSEM), Neuchâtel, Switzerland,provided technical guidance in developing the FM-UWB simulator. Dr.Sc. AndrewFort, GreenPeak Technologies B.V., Antwerp, Belgium and Dr.Sc. AttaphongseTaparugssanagorn, Asian Institute of Technology, Bangkok, Thailand shared theirchannel models used in the ultra wideband (UWB) simulations. Prof. Lorenzo Mucchi,University of Florence, Italy, shared his interference model used in the SmartBANsimulations.

The author wrote all the Papers I–VI, developed the Matlab simulators, and wasresponsible for the simulation results and related evaluations presented in Papers I–VI.

The work for this thesis was mainly conducted in three projects during the years2007–2010 and 2016–2017. The Academy of Finland co-funded the DWHN (Depend-able Wireless Healthcare Networks) project during the years 2013–2016, the Centre

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for Wireless Communications funded its internal project WiMeC (Wireless MedicalCommunications) during the years 2007–2010, and the European TelecommunicationsStandards Institute (ETSI) funded STF511 (Specialist Task Force 511) during the year2016.

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Contents

AbstractTiivistelmäPreface 9List of symbols and abbreviations 11List of original publications 19Contents 211 Introduction 23

1.1 Wireless body area networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

1.2 Author’s contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25

1.3 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2 Considerations of WBAN communications 292.1 Design considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.2 Regulations for WBAN communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.2.1 Radio frequency spectrum regulations . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.2.2 Transmission power limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.3 Functional requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3 WBAN standards 373.1 IEEE 802.15.6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.1.1 2.4 GHz narrowband PHY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38

3.1.2 Ultra wideband PHY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38

3.1.3 IEEE 802.15.6 MAC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.2 ETSI SmartBAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.2.1 SmartBAN PHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.2.2 SmartBAN MAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.3 Analysis of the IEEE 802.15.6 and SmartBAN standards . . . . . . . . . . . . . . . . . 41

3.3.1 IEEE 802.15.6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.3.2 SmartBAN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42

3.4 Other radio technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.5 FM-UWB and SmartBAN – State-of-the-art . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.5.1 Previous FM-UWB studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.5.2 Previous SmartBAN studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

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4 Framework of the research 494.1 Ultra wideband signal model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.1.1 Frequency modulated UWB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504.1.2 Direct sequence UWB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

4.2 SmartBAN signal model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544.3 Interference models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4.3.1 Interference model for UWB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 564.3.2 Interference model for 2.4 GHz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4.4 Applied channel models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 584.4.1 Hentilä’s model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 584.4.2 Fort’s model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604.4.3 IEEE 802.15.6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624.4.4 Taparugssanagorn’s model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644.4.5 Summary of the applied UWB WBAN channel models . . . . . . . . . . . . 65

5 Summary of the original publications 695.1 Ultra wideband PHY — FM-UWB and DS-UWB . . . . . . . . . . . . . . . . . . . . . . . 695.2 Narrowband PHY — ETSI SmartBAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

6 Conclusion 796.1 Main findings and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 796.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

References 83Appendices 91Original publications 93

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1 Introduction

"When wireless is perfectly applied the whole earth will be converted into a huge brain."

by Nikola Tesla, 1926

The value of the global wearables market is estimated to be USD 5.31 billion in 2016and is projected to reach USD 12.14 billion by 2021 [1]. There are several key verticalmarkets for the wireless body area network (WBAN) technology, which include themedical, sports, military, and entertainment sectors. These contain several applications,as introduced, for example, in [2–13]. Some of the applications are summarized inFigure 1.

In the sports sector, for example, there are fitness monitoring applications designedto measure heartbeat, blood pressure or respiratory rate, analyze the content of sweat,and improve the fitness and sports experience. In addition, motion capturing techniquescan be applied to analyze movements and to enhance technique, or using to assessemotions of racing drivers to avoid incidents. In a desert battlefield, readiness andefficiency of a soldier can be ensured by monitoring body hydration and fatigue canbe assessed based on behavior analysis and body signals. In the entertainment sector,WBAN can be employed in applications such as interactive gaming, information sharing,and social networking.

The most promising market sector for WBAN is healthcare. A major and the fastestgrowing application segment is diagnostics and monitoring. This application segment isdriven by an increasing need for regular monitoring and early diagnosis of patients withgrowing prevalence of chronic diseases. Other key drivers for the wearable medicaldevice market include the increasing aging of the population, changes in lifestyles, andincreasing awareness about wellness and fitness. [1]. Several non-intrusive sensorsmeasuring vital signals at the hospital or at home allow doctors to analyze results in aremote or continued fashion. The technology can be applied to automatic diagnosticsand emergency services in illnesses such as cerebral infarction. An infarction can bepredicted based on warning strokes called transient ischemic attacks [14].

It is estimated that non-communicable diseases, such as cardiovascular disease,chronic respiratory disease, cancer, and diabetes, which account for 63% of all deaths,will cost USD 30 trillion to the global economy between 2011 and 2030 [15]. In addition,the increasing number of elderly people with multiple chronic conditions is significantly

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raising the healthcare costs [16]. One of the most serious global health emergencies ofthe 21st century is diabetes [17]. It is estimated that 425 million people are currentlyliving with diabetes and 318 million people have impaired glucose tolerance. Due to itschronic nature, diabetes is an expensive illness for both the individual itself and thehealthcare system [13]. Chronic illnesses do not exclusively result from ageing but theyalso result from inappropriate diets and lifestyles and inadequate physical activity. Thepositive impacts of a proper diet and exercise on chronic diseases are discussed, forexample, in [18, 19].

The common problem with all fatal non-communicable diseases is that they arenot diagnosed in an early stage. For example, one important vital signal to measure isthe heart rate. A study published in the European Heart Journal [20] followed a totalof 9,190 hypertensive patients. It was observed that if the heart rate at rest was 84beats per minute or more, there is a 55% greater risk of cardiovascular death and a 79%greater adjusted risk of all-cause mortality. These results support continuous heartbeatmonitoring of hypertensive patients. Continuous monitoring of vital signals can beimplemented by affordable and proactive wearable monitoring systems using wirelesstechnologies specifically designed for body area networks.

WBAN Application Segments

EntertainmentHealthcare Sports Military

Medical monitoring

Automatic diagnostics

Emergency services

Telemedicine systems

Fitness monitoring

Sports training analysis

Emotional assessment

Interactive gaming

Information sharing

Social networking

Audio

Battle readiness

Emergency services

Fatigue assessment

Fig. 1. WBAN application segments.

1.1 Wireless body area networks

The concept of WBAN was first introduced by Van Dam et al. in [21]. It was moreabout connecting consumer electronic gagdets carried on a body to each other thantransmitting measured body signals. WBAN is nowadays defined as ’a short-range (i.e.,

about human body range), low power, and highly reliable wireless communications for

use in close proximity to, or inside, a human body’ [22]. WBAN is formed by severalnodes and a hub device, typically following the star network topology. A node in or on ahuman body has sensor/actuator and communications capabilities, whereas the hubdevice coordinates the node devices and communicates with devices outside the WBAN.

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Due to the new medical aspect of WBANs, the requirements and challenges are moredemanding than in conventional wireless sensor networks (WSNs). The differencesbetween these two networks are discussed, for example, in [4–6, 23, 24]. There aredifferences, for example, in the architecture, node density, data rate, latency, and mobilitypattern. The key requirement for a WBAN communications system is to ensure fast,efficient, reliable, and cost-effective monitoring and follow-up. A node device hasto be small in size, comfortable, and easy to use. Efficient communications needs tobe secure, reliable, fast, fault-tolerant, scalable, interference-immune, and low-power[25]. For these reasons, the new standard for WBAN communications was releasedby the Institute of Electrical and Electronics Engineers (IEEE) [22]. This standardwas still considered too complex, and the European Telecommunications StandardsInstitute (ETSI) established the Technical Committee (TC) SmartBAN to define aEuropean standard for a low-complexity WBAN [26].

The IEEE 802.15.6 standard defines physical (PHY) layers for narrowband andUWB communications [22]. The narrowband PHY operating at 2.4 GHz is alreadystudied, for example, in [27–29]. The mandatory PHY for UWB is the impulse radioUWB (IR-UWB), and the frequency modulated ultra wideband (FM-UWB) is optional.A comprehensive review of the IR-UWB PHY is given, for example, in [30, 31]. TheSmartBAN system applies a narrowband techology at 2.4 GHz. There are also othernarrowband systems operating at 2.4 GHz, but these are not exclusively designed forWBANs.

1.2 Author’s contribution

The main area of research in this thesis covers the study of two different PHY layers forWBAN communications. The ETSI SmartBAN system corresponds to a narrowbandsystem in the 2.4 GHz frequency band [32], whereas the FM-UWB system representsthe UWB system defined in the IEEE 802.15.6 standard [22]. The common themethroughout the original papers is to study a body-to-body and a body-to-externallink level performance of these systems. The research focused on the performanceinvestigation on the basis of the developed software simulators. The research questionsare:

– Q1: How does the FM-UWB perform in a body area network (BAN) channel relatedto direct sequence ultra wideband (DS-UWB) technology?

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– Q2: How does the selection of a channel model impact the FM-UWB performance?– Q3: How does the FM-UWB operate in an interfered BAN channel?– Q4: How does the SmartBAN PHY perform in a BAN channel?– Q5: How does the SmartBAN PHY operate in an interfered BAN channel?

A comprehensive set of link level analyses with simulations have been performedusing various WBAN channel models. When operating within the crowded 2.4 GHzindustrial, scientific and medical (ISM) frequency band, or when using a wide share of aspectrum, as in the case of UWB, interference from other systems is inevitable. Thisissue is also considered in this thesis. The performance measure used in the link levelsimulations is bit error rate (BER) or/and frame error rate (FER). Matlab simulatorswere used in all simulations.

The author of the thesis has implemented the FM-UWB system and embeddedapplied channel models into the simulator. The author was selected as a specialist forthe ETSI Specialist Task Force 511 (STF511) to model the SmartBAN system andstudy its performance. The author created the SmartBAN simulator and embedded theinterference block created by Prof. Lorenzo Mucchi, University of Florence, Italy, intothe system under the same STF511 project. The interference block model is based onmeasurements carried out at the Oulu University Hospital, Oulu, Finland [33]. Thedefinitions of the parameters and interfaces of the interference block were part of theauthor’s contribution in STF511. In addition to the simulator development, the authorcarried out the comprehensive set of simulations and analyzed the results.

Papers I–IV were written based on the FM-UWB study. As it will be pointed out inthe literature review, the FM-UWB system has not yet been studied to the extent aspresented in this thesis. The present thesis applied a large set of system parameters,error contol coding, various channel models, and interference.

Papers I–II report the results of the studies assessing the suitability of the FM-UWBfor WBAN communications and give an answer to the research question Q1. Paper IIIanswers to Q2 and indicates that the choice of the channel model applied to simulationsplays a crucial role in the system’s performance. In a hospital environment, there arealso other wireless systems operating, and therefore the research question Q3 was set.The results and analysis are provided in Paper IV.

The research questions Q4 and Q5 are similar to Q1 and Q3 but examine narrowbandcommunications. Paper V and Paper VI present the simulations results concerning the

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performance of the ETSI SmartBAN system. These results are the first of their kind,since the performance of the SmartBAN system has not been examined in earlier papers.

1.3 Outline of the thesis

This thesis investigates the link level performance of two PHY layers in an interferedWBAN channel, i.e., the FM-UWB and SmartBAN PHY layers.

Chapter 2 discusses WBAN communications, design challenges, radio frequencyspectrum regulations, transmission power limitations, and the relevant standards. Thehuman body sets a great number of challenges for wireless communications. Atemperature rise in tissue due to emitted power of a device must also be considered.There are two main standards when considering WBAN communications, IEEE 802.15.6and ETSI SmartBAN. These are discussed in detail in Chapter 3. WBAN applicationsand their requirements are also discussed. It is obvious that different applicationshave their unique requirements, for example, for latency, error rate, range, and powerconsumption. For some applications, narrowband communications fits better, and forother applications, UWB can provide the best performance.

Chapter 4 introduces a framework for the research. The simulator models developedduring the research are presented. The transceiver structures of the FM-UWB andSmartBAN simulators are discussed in detail. The DS-UWB system is also introduced,since it is applied as a reference model for the FM-UWB simulations. In addition, theapplied channel models are presented. As it will be shown, the parameters characterizingthe channel models differ considerable from each other. This has an impact on thesystem performance.

Chapter 5 provides the summary of the original papers. The thesis is concluded inChapter 6. The main results and possible topics for future research are discussed, aswell as the usability of the studied systems for WBAN applications. The original papersare provided in the appendices.

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2 Considerations of WBAN communications

2.1 Design considerations

Operation in the close proximity of the body and the variety of applications andrelated requirements set a great number of design considerations and challenges forWBAN systems [4–6, 8, 9, 11, 13, 23, 25, 34]. In WBAN communications, the humanbody becomes a critical part of a radio channel, and the WBAN environment is verychallenging for wireless communications [35].

A radio signal propagates around the human body via diffractions and reflectionsfrom arms, legs, and surrounding space. A direct signal through the body is highlyattenuated [35]. Mobility and postural body movement, variations in the permittivityof human tissue, and the complex shape of the human body generate variations to achannel, making the WBAN channel highly situation-specific.

The transmitted power needs to be kept as low as possible due to a possibletemperature rise of in tissue, and to the power consumption needs to be minimized,since data transmission consumes the most of the energy [7, 9]. A low transmissionpower minimizes the inter-BAN interference, and also improves the security and privacysince signals cannot be intercepted from a distance.

If WBAN is operating within an unlicensed frequency band, there are differentwireless technologies operating in the same band causing coexistence issues [25, 36]. Ifthere are no sufficient interference mitigation solutions implemented, this can disruptWBAN communications, making it unreliable.

A WBAN network is very heterogeneous in terms of node capabilities, resources,and applications. Some nodes may have more memory and better power supply andcomputational power than others. Traffic types vary from instant and burst emergencydata to periodic monitoring data. An application may be delay-sensitive or require ahigh packet delivery ratio.

Not only communications sets its own requirements for a system, but also usersand treatment situations impose their own demands. In patient monitoring, the idealsituation is gained when sensors are attached to a patient only once at the beginning ofthe monitoring without removing them at any time during the monitoring. To meet theserequirements, the sensors should be simple and have low power consumption, i.e., they

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should be inexpensive and have a long lifetime. To ensure patient convenience, thesensors should be comfortable and small in size.

2.2 Regulations for WBAN communications

2.2.1 Radio frequency spectrum regulations

European regulations

There are several frequency bands available for use for the WBAN communicationsdefined in the IEEE 802.15.6 standard [22]. Table 1 introduces the European radiofrequency spectrum regulations for the frequency bands used in the standard. Theregulations are based on the Electronic Communication Committee (ECC) and EuropeanRadio Communications Committee (ERC) recommendations (REC) and decisions(DEC). The following parameters are used in Table 1:

EIRP Effective Isotropic Radiated Power is the transmitted power with an absoluteantenna gain considered. [37]

ERP Equivalent Radiated Power is the transmitted power with a relative antenna gainconsidered. [37]

AFA Adaptive Frequency Agility is required ability of a device to change an operatingfrequency channel. [38]

DAA Detect and Avoid. Before initiating communications, the device monitors theradio frequency (RF) environment to detect any victim signal. The device is ableto detect any change of the RF configuration and react accordingly. [39]

DC Duty Cycle is the ratio of the cumulative duration of transmission within anobservation interval. [38]

LBT Listen Before Talk. The device can transmit at any time if a channel is sensed asfree. [40]

LDC Low Duty Cycle. The duration of a transmitted burst is less than 5 ms, and theaggregated transmission duration is less than 18 s per hour. [39]

In addition to the above, BW refers to bandwidth. The difference between the equivalentradiated power (ERP) and effective isotropic radiated power (EIRP) is that the antenna

30

gain used in ERP is relative to an ideal half-wave dipole antenna, which is 2.15dBi, whereas EIRP uses the gain of an ideal isotropic antenna. Therefore, ERP canbe converted into EIRP by adding 2.15 dBi (or by multiplying ERP by 1.64 on alinear scale). [41] The EIRP values are used in Table 1 and Table 2 to harmonize thepresentation with an exception for UWB that applies power spectral density (PSD) tolimit the transmission power.

Table 1. European radio frequency spectrum regulations for WBAN.

PHY Layer Frequency band Transmission powerlimit

Spectrum access,mitigation and

bandwidthrequirements

Document

Narrowband

402–405 MHz 41 µW — ERC/DEC/(01)17

433.05–434.79 MHz 16.4 mW ≤ 10% DC ERC/REC 70-03

1.64 mW –13 dBm/10 kHz forwideband

modulation ( > 250kHz BW)

16.4 mW ≤ 25 kHz BW

863–870 MHz 41 mW ≤ 0.1% DC or LBTor LBT+AFA

depending onbandwidth and

modulation

ERC/REC 70-03

915–921 MHz 41 mW ≤ 0.1% DC ERC/REC 70-03

2400–2483.5 MHz 10 mW — ERC/REC 70-03

100 mW Adequate spectrumsharing

mechanisms

UWB

3.1–4.8 GHz –41.3 dBm/MHz LDC or DAA ECC/DEC/(06)04

6.0–8.5 GHz –41.3 dBm/MHz —

8.5–9.0 GHz –41.3 dBm/MHz DAA

The ultra low power active medical implant (ULP-AMI) is operating in the frequencyband 402–405 MHz on a secondary basis [42]. The other bands below 1 GHz are

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reserved for short range devices (SRDs) with varying power limits and requirements[43]. Non-specific SRDs (10 mW) and wideband data transmission systems (100 mW)[43] may operate within the frequency band 2400–2483.5 MHz. The wideband systemscan use a higher transmission power only if adequate spectrum sharing mechanisms areapplied.

The generic UWB regulations define the frequency band 6.0–8.5 GHz to be mainlyintended for UWB devices [39]. Other bands are available when using the LDC orDAA techniques. The power spectral density limit of –41.3 dBm/MHz correspondsto the transmission power of –14.3 dBm for the 499.2 MHz 3 dB channel bandwidthused in the IEEE 802.15.6 standard. The generic regulations do not cover devices orinfrastructure used at a fixed outdoor location or connected to a fixed outdoor antenna.

The regulations for UWB communications are specified in more detail for roadand rail vehicles [39], location tracking [44], location tracking for emergency anddisaster situations [45], and on-board aircraft applications [46]. The most recent anda more detailed overview of the global generic UWB regulations can be found from[47, 48], and the history of the radio frequency spectrum regulations concerning UWBis summarized in [30].

FCC regulations

The rules and regulations in the United States are governed by the Federal Communica-tions Committee (FCC). Title 47 of the Code of Federal Regulations (CFR) definesthe rules for telecommunications [49]. The regulations related to the IEEE 802.15.6standard are summarized in Table 2, where the specific section of Title 47 for eachfrequency range is also specified. The transmission power limit is expressed as EIRP forother than the UWB which applies the PSD limit.

Devices operating in the 402–405 MHz, 403.65 MHz, 426–432 MHz, 438–444 MHz,and 2360–2400 MHz bands are known as Medical Device RadiocommunicationsService (MedRadio) devices. MedRadio transmitters can only be operated by dulyauthorized health care professionals. Channel monitoring is used in the 402–405 MHzband, where MedRadio transmitters are operating under a control system that monitors achannel or channels the transmitter intends to occupy. Bandwidth monitoring includes amechanism for monitoring an authorized bandwidth of a frequency band that MedRadiotransmitters intend to occupy.

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For the ISM bands 902–928 MHz and 2400–2483.5 MHz, the power limit is definedas a maximum conducted output power with the maximum antenna gain of 6 dBi. Thisis converted to EIRP by adding the antenna gain to a given output power [41]. Operationin these bands is limited to frequency hopping (FH) systems and digitally modulatedradiators.

UWB systems are defined to operate mainly indoors within the frequency bandfrom 3.1 GHz to 10.6 GHz [49]. Communications outdoors is permitted if antennas aremounted only on hand-held UWB devices. Hand-held devices are portable devices thatdo not employ a fixed infrastructure.

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Table 2. FCC radio frequency spectrum regulations for WBAN.

PHY Layer Frequency band Transmission powerlimit

Spectrum access,mitigation and

bandwidthrequirements

Document

Narrowband

402–405 MHz 25 µW Channel monitoring,≤ 300 kHz BW

Part 95I

403.65 MHz 100 nW ≤ 300 kHz, ≤ 0.01%DC max. 10

transmissions perhour

Part 95I

426–432 MHz min(0 dBm, 10log(BW)−7.782

dBm)

Bandwidthmonitoring

Part 95I

438–444 MHz min(0 dBm,10log(BW)−7.782

dBm)

Bandwidthmonitoring

Part 95I

902–928 MHz 4 W Digital modulationtechnique or FH

system with morethan 50 channels

Part 15C

2360–2390 MHz min(0 dBm,10log(BW) dBm)

— Part 95I

2390–2400 MHz min(13 dBm,16+10log(BW) dBm)

— Part 95I

2400–2483.5 MHz 4 W Digital modulationtechnique or FH

system with morethan 75 channels

Part 15C

0.5 W FH system with lessthan 75 channels

UWB

3.1–10.6 GHz −41.3 dBm/MHz — Part 15F

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2.2.2 Transmission power limitations

Since WBAN devices are used in the close proximity or within the body, it increasesthe risk of exposure to RF electromagnetic fields. Tissue absorbs RF electromagneticenergy, which leads to a temperature rise in tissue. Energy absorption of tissue isdefined based on the specific absorption rate (SAR) related to the internal electricfield as ε = σ |E|2 /ρ W/kg, where σ is the conductivity of the tissue, ρ is the massdensity of the tissue and E is the electric field strength. In an ideal nonthermodynamicsystem where there are no heat losses due to thermal diffusion, heat radiation orthermoregulation, the SAR of 1 W/kg associates with a temperature rise of less than0.0003°C/s in muscle tissue. [50]

To assure the safe use of electromagnetic energy, Europe has adopted the localexposure SAR of 2.0 W/kg in 10 g of tissue [51, 52]. In the United States, the localSAR of 1.6 W/kg in 1 g of tissue has been specified in [53, 54]. The SAR requirementslimit the maximum transmission power for WBAN at 20 mW (13 dBm) in the EU and1.6 mW (2 dBm) in the United States.

2.3 Functional requirements

There is a broad range of WBAN applications having a variety of different functionalrequirements, as can be seen from Table 3 [2, 9, 24, 55, 56].

Table 3. Functional requirements for some WBAN applications.

Application Duty cycle Bit rate Latency BER

Glucose level monitoring < 1% < 1 kbps < 250 ms < 10−10

Electrocardiography (12 leads) < 10% 144 kbps < 250 ms < 10−10

Capsule endoscope < 50% 1,000 kbps < 350 ms < 10−10

Audio < 100% 1,000 kbps < 20 ms < 10−5

Video < 100% < 10 Mbps < 100 ms < 10−3

Duty cycle and bit rate requirements vary depending on the application and the typeof data to be transmitted. Vital signal monitoring operates with a low duty cycle and bitrate, whereas audio and video streaming needs continuous transmission and a high bitrate. Latency and reliability requirements change from an application to another but, forexample, instant and high reliable reaction is needed for medical emergency service

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applications. In additon to these requirements, the typical range of WBAN applicationsis similar to that of the human body, i.e., 1.5–3 meters.

In general, power consumption needs to be minimized. Implanted nodes musthave a very low power consumption to maintain the operation for months or yearswithout interruption. Other nodes may require a battery life of hours or days. Thegeneral requirement for non-medical applications is to have a stand-by power capacityof 100–200 hours and an active power capacity of several hours [57].

Based on the application requirements, IEEE 802.15.6 and SmartBAN have definedthe technical requirements provided in Table 4 [3, 57].

Table 4. IEEE 802.15.6 and SmartBAN technical requirements.

Parameter IEEE 802.15.6 SmartBAN

Network size Up to 256 nodes (typically 6) Up to 16 nodes (typically 8)

Bit rate 10 kbps ... 10 Mbps Up to 1 Mbps

Range < 3 m < 2 m

Reliability PER < 10% (256 bytes) with a linksuccess probability of 95%

Robust to shadowing and multipathinterference

Latency < 125 ms (medical) and < 250 ms (other) 10 ms (high priority transmissions),approx. 100 ms (regular traffic)

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3 WBAN standards

This chapter introduces two standards specially designed for WBAN. The IEEE 802.15.6standard [22] includes a shared medium access control (MAC) layer for narrowband,UWB, and human body communications (HBC) PHY layer specifications, whereas theforthcoming SmartBAN standard focuses currently on the narrowband specification[32].

3.1 IEEE 802.15.6

The IEEE 802.15.6 is the international standard for communications in the proximityor within the human body to serve a variety of medical, consumer electronics, andentertainment applications [22]. It defines a MAC layer that supports narrowband,ultra wideband, and human body communication PHY layers. The standard definesa logical set referred to as a BAN, where one hub coordinates a number of nodes. Inaddition to a one-hop star network topology, the standard defines a two-hop extendedstar BAN where frame exchange between the hub and a node occurs via a relay-capablenode. Coexistence and interference mitigation between adjacent or overlapping BANsare considered by providing optional mechanisms to overcome them. There is alsoa possibility to use a hybrid automatic repeat request (HARQ) for the UWB PHY intransmitting and receiving a frame. The UWB PHY can operate either in a default ora high quality-of-service (QoS) mode. The high QoS mode is used for high-prioritymedical applications. For that purpose, the timing parameters of the PHY layer servicedata unit (PSDU) are optimized and the highest user priority is used.

The IEEE 802.15.6 standard defines seven frequency bands for narrowband commu-nications, which are 402–405 MHz, 420–450 MHz, 863–870 MHz, 902–928 MHz,950–958 MHz, 2360–2400 MHz, and 2400–2483.5 MHz. UWB communications occurswithin the 3.2448–10.2336 GHz frequency band. The third PHY is HBC that usesthe electric field communications technology at 21 MHz. In the following sections,the 2.4 GHz narrowband and UWB specifications applied to operating in unlicensedfrequency bands are discussed in detail.

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3.1.1 2.4 GHz narrowband PHY

The 2.4 GHz PHY operates with 1 MHz channels having the center frequency offc = 2402+1nc MHz, where nc = 0 . . .79 is the channel number. The PSDU is eitheran uncoded or encoded MAC protocol data unit (MPDU). An encoder applies a Bose-Chaudhuri-Hocquenghem (BCH) (n,k) code where n is the codeword length and k is themessage length, i.e., BCH(63,51). The shortened BCH(31,19) code derived from theBCH(63,51) is used for a PHY layer convergence protocol (PLCP) header. The 2.4 GHznarrowband PHY applies the π/2-differential binary phase-shift keying (DBPSK) orπ/4-differential quadrature phase-shift keying (DQPSK) with the symbol rate of 0.6MSps. Each bit of the PSDU can be spread two or four times, and the PLCP header isalways spread four times. Spreading is followed by a bit interleaver. The PHY layerapplies the square root raised cosine (SRRC) pulse shape. A transmitted PHY layerprotocol data unit (PPDU) consists of a PLCP preamble of 90 bits and a PLCP header of124 bits (4x31 bits). The PLCP preamble and header are transmitted at the informationrate of 91.9 kbps, and thus the duration of the preamble is 0.979 ms and 1.349 ms for theheader.

3.1.2 Ultra wideband PHY

The standard includes two UWB technologies, IR-UWB and FM-UWB. The hubimplements either an IR-UWB transceiver or both. A node can have either an IR-UWBor FM-UWB transceiver or both. The UWB PHY operates with 499.2 MHz channelshaving the center frequency of fc = 3494.4+499.2nc MHz, where nc = 0 . . .10 is thechannel number. Channel numbers 0, 1 and 2 are low band channels, and the remainingchannels are called high band channels. The IR-UWB has the mandatory data rate of487.5 kbps and has to use on-off signaling, whereas the FM-UWB has to support 250kbps and continuous phase binary FSK (CP-BFSK) modulation, as well as widebandfrequency modulation (FM).

The UWB PHY frame format (PPDU) consists of the synchronization header (SHR),physical layer header (PHR), and a PSDU. The PSDU is encoded with the BCH(63,51)code in the default mode, and with the BCH(126,63) in the high QoS mode. TheSHR transmission time is 40.32 µs for the IR-UWB in the default mode, 80.64 µsfor the IR-UWB in the high QoS mode, and 252 µs for the FM-UWB, and the PHR

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transmission time is 82.052 µs for the IR-UWB in the default mode, 186.67 µs for theIR-UWB in the high QoS mode, and 160 µs for the FM-UWB.

3.1.3 IEEE 802.15.6 MAC

In the IEEE 802.15.6 standard, a channel is divided into beacon periods (superframes)containing 0–255 slots of equal duration [22]. There are three access classificationsdefined: beacon and non-beacon modes with superframe boundaries and a non-beaconmode without superframe boundaries. In the beacon mode with superframe boundaries,the superframe structure is divided into a beacon transmitted by the hub, the exclusiveaccess phase (EAP), the random access phase (RAP), the managed access phase (MAP),and the contention access phase (CAP), as illustrated in Figure 2. Polling frames areused by the hub to define the superframe boundaries for non-beacon modes. The EAP isreserved for high-priority or emergency traffic, whereas the RAP and CAP are intendedfor nonrecurring traffic. The MAP is used for determining allocations. Depending onthe PHY, the hub may employ the slotted ALOHA or carrier sense multiple access withcollision avoidance (CSMA/CA) in the EAP, RAP, and CAP periods.

In addition to the slotted ALOHA and CSMA/CA, the standard defines other accessmechanisms. In the improvised and unscheduled access, the hub may use improvisedaccess to send poll or post commands without prereservation or advance notice. Onthe other hand, the scheduled and scheduled-polling access mechanisms are used toobtain scheduled uplink, downlink, and bilink allocations, as well as polled and postedallocations.

In the non-beacon mode with superframe boundaries, the hub operates during theMAP period only. The polled or posted allocations, or a combination of both, areprovided by the hub in the non-beacon mode without superframe boundaries.

Bea

con

Bea

con

EAP1

Superframe period

RAP1 MAP EAP2 RAP2 MAPCAP

Fig. 2. IEEE 802.15.6 superframe for beacon mode.

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3.2 ETSI SmartBAN

The ETSI TC smart body area network (SmartBAN) established in 2013. The TC hasreleased technical specifications for an ultra-low power PHY [32], a low complexityMAC [58], and data representation formats and a data model [59]. The TC hasalso introduced smartness to the network control, management, heterogeneity, andinteroperability [3]. SmartBAN specifies 2.4 GHz on-body communications linksbetween nodes and a hub (coordinator) following a star network topology. The hub alsoserves as an intermediate gateway node allowing an interconnection between a clusterand a remote monitoring and control center. A new work item (WI) was launched in theSmartBAN meeting in May 2016 to define the functionalities required to support alsotwo-hop communication inside the SmartBAN network [60]. The focus areas of theWI include the relay node functionality for two-hop communication and support forhub-to-hub communications.

3.2.1 SmartBAN PHY

The SmartBAN system is operating within the frequency band from 2410 MHz to 2481MHz, and it is arranged in the blocks of 2 MHz referred to as channels. Each channelhas the center frequency of fc = 2402+2nc MHz, where nc = 0 . . .39 is the channelnumber [32].

A transmitted PPDU is formed of a PLCP preamble (16 bits), a PLCP header (40bits), and a PSDU. The PSDU is either an encoded or uncoded MPDU. The MPDU isencoded with the BCH(127,113) code and the PLCP header with the BCH(36,22) code.It is remarkable that the preamble and the header take only 56 bits in SmartBAN, whichis approximately one-fourth of the corresponding number in the IEEE 802.15.6 PHY.The modulation is the Gaussian frequency shift keying (GFSK) with the bandwidth-symbol time product BTs = 0.5 and the modulation index h = 0.5. The symbol rate Rsym

is fixed to 1 MSymbols/s. SmartBAN utilizes the PPDU repetition to reduce errors. Inthe PPDU repetition, the entire PPDU frame is repeated two or four times.

As a careful reader may notice, the SmartBAN PHY is quite similar to the Bluetooth4.0 low energy (LE) PHY [61]. The major difference between the two at the PHYlayer is the lack of frequency hopping in SmartBAN. In addition, SmartBAN includesrepetition and error correction coding schemes.

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3.2.2 SmartBAN MAC

The logical channels applied in the SmartBAN system are the data channel (DCH)and control channel (CCH), and each SmartBAN node utilizes one CCH and one DCHat a time [58]. Transmission over the CCH is utilized only by the hub, which sendsone control channel beacon (C-Beacon) every TC seconds. The DCH is divided intointer-beacon intervals (IBIs) lasting TD seconds, and it is bounded the transmitteddata channel beacon (D-Beacon) transmitted by the hub. Each IBI is further dividedinto LD slots having the duration of LDTS, where TS = LslotTmin and Tmin = 625 µs andLslot =[1,2,4,8,16,32].

The DCH-IBI has four periods: the beacon period, scheduled access period, controland management access period, and inactive period. In the beacon period, the hubtransmits a D-Beacon within one single timeslot. The scheduled period consists of NS

time slots, the unscheduled control and management period has NCM time slots, and notransmission occurs in the inactive period. The structure of the DCH-IBI is illustrated inFigure 3.

In SmartBAN, there are three types of channel access mechanisms:

1. Scheduled. In this mode, there are allocated slots to transmit frames. Transmissionoccurs only in the scheduled access period.

2. Slotted ALOHA. This channel access mode takes place in the control and managementperiod, if the hub or node does not have enough allocated time slots in the scheduledaccess period to transmit its data. The node maintains contention probability tofacilitate channel access.

3. Multi-use. In the optional multi-use channel access mode, a channel is sensed beforetransmission. The duration of the sensing period depends on the slot ownershipand the type of traffic. In this case, the node may transmit if the channel is idle.Otherwise, it starts the backoff counter routine.

3.3 Analysis of the IEEE 802.15.6 and SmartBAN standards

3.3.1 IEEE 802.15.6

The strengths of the IEEE 802.15.6 standard are:

– Standardization: The IEEE 802.15.6 standard was the first dedicated WBAN standardand was completed in 2012.

41

D-B

eaco

n

D-B

eaco

n

......

Scheduled access period (NS*TS)Control and management

period (NCM*TS)Inactive period

Inter-beacon interval (TD=LD*TS)

TS

...

TS

Tmin

1 2 3 4 LSLOT

Fig. 3. Structure of the SmartBAN IBI.

– PHY Diversity: The standard covers multiple PHY layers, covering the narrowband,ultra wideband, and human body communications.

– Two-hop: The standard defines a two-hop star network architecture with relay-capablenodes.

– WBAN coexistence and interference mitigation mechanisms: The mechanisms forcoexistence and interference mitigation between adjacent or overlapping BANsare provided. These are beacon shifting, channel hopping, and active superframeinterleaving.

On the other hand, the weaknesses can be seen in areas such as:

– Protocol overhead: The narrowband PHY layer headers take 214 bits, which is arelative large share for small packets.

– Hardware: Similar as SmartBAN, IEEE 802.15.6 suffers from unavailability ofcommercial devices but, for example, an experimental multistandard system onchip (SoC) for narrowband Bluetooth LE and IEEE 802.15.6 has been introduced in[62, 63].

– Coexistence and interference mitigation mechanisms for other systems: The standarddoes not give any recommendations for coexistence or interference mitigationtechniques to avoid cross-interference from other systems than WBAN.

3.3.2 SmartBAN

The SmartBAN standard includes not only the PHY and MAC layers but also higherlevel system aspects. It provides a comprehensive view from the link level to data

42

transfer and heterogeneity management. The strengths of the standard include thefollowing [3]:

– Protocol overhead: The PHY layer headers are squeezed into 56 bits, making thePHY protocol overhead small.

– Channel access: SmartBAN defines a low complexity MAC layer. There is a uniquemulti-use channel access designed for a very low latency emergency messaging fortime-critical applications.

– Smartness: The SmartBAN system introduces smartness, for example, to networkcontrol, management, heterogeneity, and interoperability. Smartness in heterogeneousmanagement is achieved by introducing a common semantic approach, i.e., havingan open data model dedicated to the SmartBAN including conflict resolution andsimilarity detection.

– Low delay and energy consumption: SmartBAN has lower average delay, initial setuptime, and energy consumption than IEEE 802.15.6 [64, 65].

The main weakness of the standard relate to its coexistence with Bluetooth LE. Theweaknesses of the standard are:

– Coexistence with Bluetooth LE: SmartBAN uses the same channels as Bluetooth LE,and overlapping in the control channel may cause severe problems.

– Coexistence and interference mitigation mechanisms: Although the system is operat-ing in the crowded 2.4 GHz frequency band, no coexistence or interference mitigationtechniques are recommended yet.

– Lack of multihop: Currently, the standard lacks multihop communications, but thework item for relay and hub-to-hub networking has been opened.

– Hardware: To the best of the author’s knowledge, there is no commercial hardwareavailable.

3.4 Other radio technologies

There are also other radio technologies operating at the 2.4 GHz or in the UWBfrequency bands that are intended for WBAN communications. ZigBee [66] is a lowpower wireless specification designed based on the IEEE 802.15.4 standard [67]. Itprovides additional protocol layers on top of the IEEE 802.15.4 defined PHY and MAClayers, such as network, security, and application layers. ZigBee has published a health

43

care public application profile for remote health and fitness monitoring in, for examplehospital and home environments [66].

Bluetooth LE was originally defined in Volume 6 of the core package version 4.0 in2010 [61]. Version 4.2 designed for the internet-of-things (IoT) introduced new featuresfor improved privacy and enhanced speed and internet protocol (IP) connectivity ofsensors. Version 5.0 was released in 2016, providing a longer range, higher speed,and larger advertisement packet length for data [68]. Larger advertisement packetswith offloading the advertising payload into regular data channels makes Bluetooth 5.0feasible for connectionless IoT. In addition, Bluetooth LE provides faster pairing andreconnection times than the classic Bluetooth.

There are also proprietary technologies such as ANT [69]. ANT is a wireless sensornetwork protocol and uses the 2.4 GHz ISM band. ANT+ is a wireless standard basedon the ANT technology to provide interoperability between devices from differentmanufacturers. The radio technologies for WBAN are discussed in more detail, forexample, in [9, 10, 13].

3.5 FM-UWB and SmartBAN – State-of-the-art

3.5.1 Previous FM-UWB studies

The FM-UWB system is based on the work by Gerrits et al. presented in [70–77]. Gerritset al. did a comprehensive performance study of the new system, as discussed below.In their first article [70], the FM-UWB system was introduced with a comprehensiveanalysis of the transceiver structure, parameters, and performance. It is discussed that theFM-UWB suffers from a considerable performance penalty compared to a narrowbandsystem when converting the signal-to-noise power ratio (SNR) to the bit energy tonoise power spectral density Eb/N0 due to the non-linearity of the demodulator. Thesimulation results show the performance in the additive white Gaussian noise (AWGN)and simple multipath channel, indicating robustness of the multipath channel. Theapplied channel has 8 paths with the delay spread of 6.5 ns. The multipath propagationof the FM-UWB signal results in harmonic distortion of the demodulated signal: thelarger the number of the multipath components, the greater the distortion. In addition,the multipath channel limits the useful subcarrier range without equalization, and hence,limits the number of users. Gerrits et al. also analyzed the robustness of the systemagainst narrowband interference, and concluded that the FM-UWB is resilient against it.

44

The multipath behavior of the FM-UWB signals and the effect of a frequencyselective fading channel on the sub-carrier signal level were studied more carefully in[73]. It was concluded that a good channel for the FM-UWB is such where the valuesof the channel transfer function are strong at the extremes of the FM-UWB signal. Ifthey are low, the channel is considered bad because it attenuates the amplitude of thesub-carrier.

The FM-UWB system can accommodate multiple users by introducing time divisionmultiple access (TDMA), frequency division multiple access (FDMA), or subcarrierFDMA. In the subcarrier FDMA designed for ultra low-power applications, users sharethe same RF carrier frequency but use different subcarriers [71, 74]. The FM-UWBcan tolerate strong multiuser interference when the data rate is low (100 kbps) and thereceiver processing gain is large (37 dB) [71]. For the FM-UWB, the bit rate defines themaximum number of users. With the RF bandwidth of 1 GHz, 150 users with an equalpower having the data rate of 10 kbps can be accommodated, or 15 users with 100 kbps[71].

In-band interference can be mitigated with the processing gain at the receiver,filtering, notch antenna design, and detect and avoid (DAA) techniques [74, 77]. In[75], the results from measurements where the performance of the FM-UWB systemwas measured with narrowband interference are presented. It was found that the 20 dBstronger narrowband interferer degrades the BER performance of the FM-UWB systemto 10−3.

Transceiver prototypes and circuit considerations are discussed by Gerrits et al. in[72, 74–76]. It is estimated that the power consumption of a fully integrated system is 1mW for the transmitter and 5–8 mW for the receiver [76].

Other authors have also implemented FM-UWB prototypes, see, for example,[78–82]. In [80], the fully integrated FM-UWB transceiver is presented. The averageenergy efficiency of the transceiver is 6 nJ/bit. Developments in low-power FM-UWBtransceiver realizations are discussed in [83].

In [84–86], Gupta et al. proposed the FM-UWB radar to be used for monitoringvital signals. The proposed system applies the multi-frequency continuous wave radarusing FM signals. The simulation results indicate that the FM-UWB system can achivethe resolution in the order of millimeters. Gupta et al. also discussed the possibility tointegrate sensing and communications capabilities using the same FM-UWB technology.

In [87], the performance results of the narrowband and FM-UWB systems using theWiseMAC protocol [88] are introduced. The narrowband system uses the 868 MHz TI

45

CC1100 radio transceiver [89]. The power consumption of transmission and reception ismuch higher for the narrowband transceiver than for the FM-UWB. The transmissionpower is 51 mW and reception power 49.2 mW for the narrowband system, whereas theFM-UWB requires 5.5 mW and 15 mW, respectively. When the FM-UWB is combinedwith WiseMAC, the power consumption below 1 mW can be reached with relativelyfrequent transmissions.

Based on the literature survey, it is noticed that the error rate performance of theFM-UWB system has not been analyzed in experimental WBAN channel models suchas the standard IEEE 802.15.6 channel. In addition, the impact of in-band UWBinterference on the FM-UWB system performance has not been analyzed by simulationsearlier. The author’s contribution to this issue is given in Papers I–IV.

3.5.2 Previous SmartBAN studies

As discussed earlier, the TC SmartBAN was established in 2013 to define an ETSIstandard for WBANs. The detailed technical requirements concerning SmartBANwere presented in the previous section 3.2. The work of the TC is divided into severalparallel work items (WIs) as presented in Table 5 [60]. For these WIs, the technicalspecifications TS 103 325 (MAC), TS 103 326 (PHY), and TS 103 378 (Data) havealready been published. TS 103 325 has been revised to include the relay nodefunctionality enabling two-hop communications within a single SmartBAN, as wellas hub-to-hub communications. DTS/SmartBAN-009 has been extended with thespecification and the formalization of the SmartBAN unified service/application levelrepresentation formats, semantic open data model, and corresponding ontology. Thelatest WIs were opened to define the technical specifications for implant communications(DTS/SmartBAN-0012) and to compare the performance between SmartBAN and otherstandards (DTR/SmartBAN-001). DTR/SmartBAN-004 gives the high level descriptionand mechanisms providing solutions for heterogeneity management. The results of thework of the STF511 are presented in TR 103 395, which evaluates the performance ofSmartBAN in its operational environment. The SmartBAN system description is givenin DTR/SmartBAN-008. The SmartBAN system is generally discussed by SmartBANcommittee members in [26, 64, 90–92]. There are only some performance studies of theSmartBAN system in the literature. Those studies focused on optimizing the MAC layerperformance in terms of latency and energy efficiency [65, 93, 94].

46

Nevertheless, before the author’s contribution, there were no previous studies on theperformance of the SmartBAN PHY layer. Papers IV and V are based on the author’scontribution in the STF511 concerning the PHY performance of the SmartBAN systemin its operational environment.

Table 5. SmartBAN work items and publications.

# Work item/publication Title Status

1 TS 103 325 Low Complexity Medium Ac-cess Control (MAC) for Smart-BAN

Published (2015-04-28)

2 RTS/SmartBAN-005r1 Low Complexity Medium Ac-cess Control (MAC) for Smart-BAN, revision

Started (2016-05-18)

3 TS 103 326 Enhanced Ultra-Low PowerPhysical Layer

Published (2015-04-28)

4 TS 103 378 Unified data representation for-mats, semantic and open datamodel

Published (2015-12-11)

5 RTS/SmartBAN-009r1 Unified data representation for-mats, semantic open data-model and corresponding on-tology. Associate servicemodel/ontology/enablers exten-sions for SmartBAN semanticinteroperability

Started (2016-05-18)

6 DTS/SmartBAN-0012 Implant communications Started (2017-05-31)7 DTR/SmartBAN-001 Comparative analysis between

SmartBAN and other short-range standards

Started (2017-02-29)

8 DTR/SmartBAN-004 Service and application stan-dardized enablers and inter-faces, APIs and infrastruc-ture for interoperability manage-ment

Stable draft (2016-05-14)

9 TR 103 395 Measurements and mod-elling of SmartBAN RadioFrequency(RF) environment

Published (2016-12-20)

10 DTR/SmartBAN-008 System Description Final draft (2017-03-31)

47

4 Framework of the research

The focus of this thesis is to study the performance of the FM-UWB and SmartBANPHY layers in a WBAN environment. The DS-UWB is applied as a reference PHYlayer for the FM-UWB. The PHY layers are analyzed by using the Matlab software.Two communications links are considered, body-to-body and body-to-external links.This chapter introduces the framework of the research, including the simulation models,used channel models, and interference models.

4.1 Ultra wideband signal model

The software simulator was developed in Matlab to study the performance of twosingle-band UWB PHY layer designs. The FM-UWB uses double frequency modulationto obtain the UWB signal [70], whereas the DS-UWB applies very short pulses tospread information generating an ultra wide spectrum [95]. The block diagram of thesimulator model is depicted in Figure 4. The data bits b[k] are either uncoded or encodedby using a BCH code. The uncoded or encoded bits are modulated and spread over alarger bandwidth. The transmitted signal s(t) propagates through a multipath channelexpressed as its impulse response h(t). The received signal r(t) is constructed from themultipath propagated signal, the interference i(t), and the AWGN n(t) having varianceσ2

n , and thus the received signal is

r(t) = s(t)∗h(t)+ i(t)+n(t), (1)

where t is time and ∗ denotes the convolutional operation. The details of the simulatorsare given in the following sections. The applied fading channels are discussed insection 4.4 and the interference models are presented in sections 4.3.1 and 4.3.2.

Inter-

ference

i(t)

Data bits

b[k]Modulation Spreading

Multipath

Channel

h(t)

Received

bits r[k]Receiver

AWGN

n(t)

s(t) r(t)d[k]BCH

Encoder

BCH

Decoder

Ui

Fig. 4. Block diagram of the UWB simulator.

49

4.1.1 Frequency modulated UWB

The FM-UWB is a very simple low data rate UWB technique where a double frequencymodulation is applied [70]. It constitutes an analog implementation of a spread spectrumsystem [96]. A data stream is modulated with a low-modulation index digital frequencyshift keying (FSK) followed by a high-modulation index FM. The modulation index isdefined as β = ∆ f/ fm, where ∆ f is the frequency deviation and fm is the frequency of amodulating signal. The modulation index can be chosen freely. A small index (β < 1)yields a narrowband FM signal, and a large index (β >> 1) generates a UWB signal.The FM signal sFM(t) modulated by the signal m(t) having the frequency fm is expressedas [70]

sFM(t) = Asin(2π fct−β cos(2π fmt)+ϕ0) , (2)

where A is the amplitude, fc is the carrier frequency, and ϕ0 is the arbitrary but time-independent constant phase. For FM-UWB, the processing gain at a receiver PGFM canbe expressed as a ratio of the RF bandwidth BRF and subcarrier bandwidth BSUB as [74]

PGFM = 10log10

(BRF

BSUB

). (3)

The signal is received using a delay-line FM demodulator followed by a FSK demodula-tor, as depicted in Figure 5. In the FM demodulator, a received signal is multipliedwith τ = Nd/(4 fc) delayed signal, where Nd = 1,3,5 . . . [70]. The operation of thedemodulator is analyzed in [70], and it is shown that the demodulator sensitivity isproportional to Nd , yielding the useful bandwidth of the demodulator BDEMOD defined as

BDEMOD =2

Ndfc. (4)

The output voltage of the FM demodulator VFM,out is given as [70]

VFM,out(t) = (−1)((Nd+1)/2) A2

2sin(

Oπ

2sin(ωmt)

), (5)

where O = Nd (∆ f/ fc) is the FM demodulator overdrive and ωm is the angular frequencyof a modulating signal.

The ratio between the deviation of the FM-UWB signal and the useful bandwidth ofthe FM demodulator is defined by the overdrive. From (5), it can be seen that when theoverdrive is less than 1, the output voltage of the demodulator does not exploit the fullavailable dynamic range. This occurs when a deviation of a FM signal is less than onehalf of the useful bandwidth of the modulator [70].

50

The FSK demodulator employs an amplitude modulation (AM) projection detection[97]. At first, it converts an input signal to zero frequency, and subsequently convertsit to an AM wave with a differentiation stage. The final stage is the AM projectiondetection.

LPF

LPF

90°

d

dt

d

dt

Uiω0

I = cos(φ)

Q = sin(φ)

+

–

Delay

element

(τ)

r(t)

Delay-line FM

demodulatorFSK demodulator

Fig. 5. Block diagram of the FM-UWB receiver (Paper IV © 2017 ACM).

4.1.2 Direct sequence UWB

The DS-UWB is an impulse radio based technology where there is no need for a signalupconversion to a higher frequency. It communicates with a baseband signal constructedfrom several subnanosecond pulses. [98] The energy of the information bit is spreadover multiple chips by applying a pseudorandom code to achieve a pulse repetitionprocessing gain PGDS defined as [95]

PGDS = 10 log10

(1

RbTc

), (6)

where Rb is the bit rate and Tc is the chip duration. The chip duration is the durationof a pulse for spreading code in this context. The simulator applies two modulationtechniques, binary pulse amplitude modulation (BPAM) and on-off keying (OOK).BPAM modulates the data bits b[k] ∈ {0,1} into the antipodal presentation d[k] ∈{−1,1}. In the case of OOK, nothing is transmitted for the bit ’0’, i.e., d[k] = b[k].Therefore, pulse energy is two-folded to maintain an average pulse energy constant.A spreading code sequence cp ∈ {−1,1}, having the length of Nc chips, is utilized tospread a signal. For each chip, two antipodal pulse waveforms xp,1(t) corresponding tothe chip ’1’ and xp,−1(t) to the chip ’−1’ are used, as

xp,1(t) =−xp,−1(t) = xp(t)cp. (7)

51

The transmitted signal sDS(t) is defined as [95]

sDS(t) =∞

∑k=−∞

Nc

∑j=1

xp(t− kTb− jTc)(cp) jd[k], (8)

where Tb is the bit duration.

Pulse waveforms

For impulse radio UWB systems, the Gaussian monocycle xG(t) is typically used as abaseband pulse waveform. It is defined as [98]

xG(t) =A√2πσ

exp(− t2

2σ2

), (9)

where σ is the standard deviation. The use of higher order derivatives and shorter pulsesgives larger bandwidth and higher center frequency. The n-th order derivative of theGaussian monocycle is represented as [99]

x(n)G (t) =−n−1σ2 x(n−2)

G (t)− tσ2 x(n−1)

G (t). (10)

The challenge of using Gaussian pulses is to find a feasible pulse length and derivativeorder which result in a desired frequency range. Therefore, another pulse waveformcalled an eigenpulse, derived in [100], is also used in comparison. The pulse designalgorithm utilizes the idea of prolate spheroidal wave functions. At first, the desiredfrequency mask Hp( f ) as a function of frequency f and its corresponding impulseresponse hp(t) is defined as [100]

Hp( f ) =

1, fl < f < fu

0, elsewhere,(11)

where fl and fu are the lower and upper frequencies of the desired frequency mask,respectively, and [100]

hp(t) = 2 fu sinc(2 fut)−2 fl sinc(2 flt). (12)

The idea is to design a time-limited pulse ψ(t) affected by minimal distortion when itpasses through the filter with the impulse response hp(t), i.e., [100]

ψ(t) =

p(t), |t|< Tm2

0, elsewhere,(13)

52

where p(t) is the pulse shape meeting Hp( f ) and Tm is the pulse duration. By using thesampling rate of N samples per Tm, the discrete-time filter output is given as [100]

λψ[n] =N/2

∑m=−N/2

ψ[m]hp[n−m], n =−N2. . .

N2, (14)

where λ is the attenuation factor [100]. Equation (14) can be expressed in the matrixform as [100]

λψψψ = Hψψψ, (15)

where H represents the Toeplitz matrix derived from hp(t) in (14). Clearly, ψψψ =

|ψ1,ψ2, . . . ,ψm| is the eigenvector of H, and thus, the desired waveform can be foundvia the eigenvalue decomposition of H and choosing an eigenvector with the largesteigenvalue [101]. Figure 6 illustrates the designed eigenpulse with fl = 3.0 GHz,fu = 5.0 GHz, and Tm = 2.0 ns. By using these parameters, the center frequency sets to4.0 GHz, −10 dB bandwidth to 1.5 GHz, and −20 dB bandwidth to 2.0 GHz.

0 0.5 1 1.5 2Time [ns]

-1.5

-1

-0.5

0

0.5

1

1.5

Nor

mal

ized

volta

ge[V

]

0 1 2 3 4 5 6 7 8Frequency [GHz]

10-10

10-8

10-6

10-4

10-2

100

Nor

mal

ized

PSD

[W/H

z]

Fig. 6. Eigenpulse.

53

Rake reception

The use of ultra wideband transmission makes it possible to resolve closely spacedmultipath components (MPCs) encountered in a channel [95]. Detection of such a signalleads to a rake receiver, which combines multipath propagated signal components, andthus provides diversity used to improve the quality of a receiver signal [102].

An all-rake (ARake) receiver captures and combines all the resolved MPCs. Since thenumber of resolvable MPCs increases with the spreading bandwidth, power consumptionand complexity of design and channel estimation increase accordingly [102]. This makesARake impractical. The selective-rake (SRake) and partial-rake (PRake) receiversprocess a subset of the available resolved MPCs. SRake selects the Nr best paths withthe highest SNR, whereas PRake takes the first Nr paths [102].

The selected MPCs can be combined by using the coherent maximum ratio combin-ing (MRC) or equal gain combining (EGC) technique or the non-coherent square-lawcombining (SLC) technique. In this thesis, MRC and SLC were chosen for investigation.MRC represents the optimal and SLC suboptimal but simple combining technique.Their decision variables Ui,MRC and Ui,SLC are given as [95]

Ui,MRC =Nr

∑n=1

a∗n

Tb∫0

r(t− τn)wi(t)dt, i = 0,1, (16)

and

Ui,SLC =Nr

∑n=1

∣∣∣∣∣∣Tb∫

0

r(t− τn)wi(t)dt

∣∣∣∣∣∣2

, i = 0,1, (17)

where an is the complex gain of the n-th MPC, ()∗ denotes the complex conjugate, τn isthe delay of the n-th MPC, and wi(t) is the transmitted pulse waveform for the data bit i.

4.2 SmartBAN signal model

The SmartBAN system was modeled by using Matlab with Simulink to evaluate thesystem performance. The simulator model is based on the PHY [32] and MAC [58]layer technical specifications. Figure 7 represents the high-level block diagram of thesimulator.

54

Data bits

b[k]

BCH

Encoder

GFSK

Modulator

s(t)

r(t)

e[k] PLCP Header

Insertion

Preamble

Insertion

PPDU

Repetition

Fading

Channel h(t)

AWGN

n(t)

Interference

i(t)

PPDU

Combining

GFSK

Demodulator

Preamble

Removal

PLCP Header

Removal

BCH

Decoder

Received

bits r[k]

Fig. 7. Block diagram of the SmartBAN simulator.

The data bits b[k] forming MPDU are encoded with the BCH(n,k) code, where n isthe codeword length and k is the message length. A PLCP header and preamble areinserted to the encoded bit stream e[k]. The SmartBAN PHY layer deploys the GFSKmodulation, where encoded bits e[k] ∈ {−1,1}, preamble, and header bits are passedthrough a Gaussian filter before the FSK modulation, which smooths the pulses andlimits the modulated spectrum width. The Gaussian pulse shape g(t) is defined as [103]

g(t) =√

π

ξe−π2t2/ξ 2

, (18)

where ξ is the parameter specifying spectral efficiency and expressed in terms of the3-dB bandwidth-symbol time product (BT ) as [103]

ξ =

√− ln√

0.5√2

TBT

. (19)

After modulation, the repetition coding may be employed to repeat the PPDU two orfour times. The transmitted signal s(t) is represented as [104]

s(t) = Acos

2π fct +2πβ

t∫−∞

m(τ)dτ

, (20)

where m(t) = ∑k e[k]g(t− kT ) is the Gaussian filtered input data signal modulated withthe encoded bit stream.

The transmitted signal propagates through the fading channel h(t), which is intro-duced in section 4.4.3, and the interference process i(t) introduced in section 4.3.2.Finally, the AWGN noise n(t) having the variance σ2

n is added to the signal. From thereceived signal r(t) = s(t) ∗ h(t)+ i(t)+ n(t), the repeated PPDUs are combined byassuming the perfect channel estimation. The GFSK demodulator applies a correlator,

55

followed by a maximum likelihood sequence detector (MLSD). The detector is imple-mented by using the Viterbi algorithm [105]. Subsequently, the preamble and the PLCPpreamble are removed, and the packet is decoded to the received bits r[k].

4.3 Interference models

4.3.1 Interference model for UWB

To study the performance of the FM-UWB system in an interfered channel, the coloredGaussian noise (CGN) is applied to model in-band and aggregate interference frommultiple IEEE 802.15.4 UWB signals. CGN is the white Gaussian noise process filteredwith a bandpass filter. It provides a tool to implement specific interference to desiredfrequencies and bandwidths by taking advantage of the band-limited characteristic ofCGN. The interference generation procedure is presented, for example, in [106].

The first step is to design a proper bandpass filter. The bandpass filter uses the raisedcosine waveform to have the baseband impulse response hrc(t) having the frequencyresponse Hrc( f ) as [105]

Hrc( f ) =

T, 0≤ | f |< 1−α

2TT2

(1+ cos

(πTα

(| f |− 1−α

2T

))), 1−α

2T ≤ | f | ≤1+α

2T

0, | f |> 1+α

2T ,

(21)

where α is the roll-off factor and T is the symbol time related to the desired bandwidthBW. The next step is to shift the filter to the desired center frequency fc to have theCGN filter hcgn(t) as [105]

hcgn( f ) = hrc(t)cos(2π fct +ϕ) . (22)

The in-band interference i(t) is generated by filtering the white Gaussian noise n(t)

with the filter hcgn having the frequency response Hcgn( f ), as illustrated in Figure 8.The interference power is adjusted according to a given signal-to-interference powerratio (SIR).

4.3.2 Interference model for 2.4 GHz

SmartBAN is operating in the 2.4 GHz ISM band, sharing it with other wirelesstechnologies such as Bluetooth, WiFi, and ZigBee. Therefore, it is important to model

56

Hcgn(f)

fc, BW, α

i(t)n(t)

𝑆𝐼𝑅

Fig. 8. Generation of the interference (Paper IV © 2017 ACM).

interference in this frequency band in the operational environment of SmartBAN. Theinterference model for the 2.35–2.50 GHz frequency band is based on the spectrumoccupancy measurements performed at the Oulu University Hospital [107–110]. Themeasurement results indicated that spectrum occupancy was at the lowest level inchannel 6 at 2.437 GHz and the highest in channel 1 at 2.412 GHz, when the channelnumbering follows the IEEE 802.11b/g standard. In the STF511 [111], the measureddata was processed to develop a statistical model of interference in the ISM bandin the hospital environment. An exhaustive introduction to the spectrum occupancymeasurements, data processing, data analysis, and model extraction is given in [33].

Three characteristics of the interference were modeled, i.e., the cluster dimension,interarrival time, and cluster amplitude. It was found out that the generalized Paretodistribution fits best to the cluster dimension for both channels. The probability densityfunction of the generalized Pareto distribution is expressed as [112]

f (x|s,η ,θ) =1η

(1+ s

(x−θ

η

))−1−1/s

, s 6= 0, (23)

for θ < x, when s > 0, or for θ < x < θ −η/s when s < 0, and

f (x|s,η ,θ) =1η

e−(x−θ)/η , s = 0, (24)

for θ < x, where s is the shape parameter, η is the scale parameter, and θ is the thresholdparameter. The parameters for channel 6 are s = 4.8, η = 2.5×10−15 and θ = 1, and forchannel 1 s = 2.43, η = 2.34×10−15 and θ = 1 [33]. The interarrival time follows thePareto distribution in channel 6 and the generalized extreme value (GEV) distribution inchannel 1. The GEV distribution is given as [112]

f (x|s,η ,µ) =1µ

e−(1+s((x−η)/µ))−1/s(

1+ sx−η

µ

)−1−1/s

, s 6= 0, (25)

57

for 1+ s((x−η)/µ)> 0 and

f (x|s,η ,µ) =1µ

e−e−((x−η)/µ)−(x−η)/µ , s = 0, (26)

where µ is the location parameter.The parameters for channel 6 are s = 0.12, η = 373.5 and θ =−2.2×10−15 and for

channel 1 s = 0.55, η =−83.7 and µ = 3.42 [33]. The best fitting distribution for thecluster amplitude is GEV for both channels. The parameters for channel 6 are s = 0 : 14,η =−83.7 and µ = 2.7 and for channel 1 s = 0 : 12, η =−79.4 and µ = 3.7. Thesedistributions are applied to generate the interference vector for the simulations. Theinterference power is adjusted according to a given SIR value.

4.4 Applied channel models

This section discusses the applied channel models focusing on the scenarios used in theresearch. Hentilä’s and Taparugssanagorn’s models were selected since they offer aUWB channel model for a hospital environment. Fort’s model was included in the IEEE802.15.4a channel model because it is the first standardized UWB channel model for theon-body BAN scenario [113]. Naturally, the reference IEEE 802.15.6 channel modelwas also selected for this study. Two communications links are considered: a link from adevice attached to body to another device attached to body (body-to-body) or to anexternal device (body-to-external). For UWB models, the channel parameters for thepower delay profile following the complex impulse response h(t) are derived, expressedas [114]

h(t) =L−1

∑l=0

al exp( jφl)δ (t− tl) , (27)

where al is the path amplitude for the l-th path, tl is the l-th path arrival time, φl is therandom phase for the l-th path modeled by a uniform distribution over [0,2π), L is thetotal number of the arrival paths, and δ (t) is the Dirac delta function. For narrowbandchannels, (27) reduces into h(t) = al(t)exp( jφl(t)) [115].

4.4.1 Hentilä’s model

Hentilä et al. carried out the measurements at the Oulu University Hospital in anoperating room (OR), an intensive care unit (ICU), and an x-ray examination room (XR),where various transmitter and receiver locations were utilized [116]. The measurements

58

were performed over the 3.1–6.0 GHz frequency band by using a vector network analyzer.Based on the data from the measurements, parameters for the modified IEEE 802.15.3achannel model were extracted. The modified IEEE 802.15.3a model is introduced in[117], where the channel impulse response is defined as

h(t) = XK

∑k=0

L−1

∑l=0

αl,kδ(t− τk− tl,k

), (28)

where αl,k is the multipath gain coefficient, τk is the delay of the k-th cluster, tl,k is thedelay of the l-the path relative to the k-th cluster arrival time τk, K is the total number ofclusters, and X represents the lognormal shadowing. The distribution of the clusterarrival time with the cluster arrival rate Λ is given by

p(τk|τk−1) = Λexp(−Λ(τk− τk−1)) k > 0 (29)

and the distribution of the ray arrival time with the ray arrival rate λ is expressed as

p(tl,k|t(l−1),k

)= λ exp

(−λ(tl,k− t(l−1),k

)), l > 0. (30)

The channel coefficients are αl,k = pl,kξkβl,k, where ξk is the fading associated with thek-th cluster and βl,k corresponds to the fading associated with the l-th path of the k-thcluster. 20 log10(ξkβl,k) are normally distributed with mean µl,k and standard deviationσ2

1 +σ22 . Signal inversion due to reflections is accounted with pl,k =±1. The expected

value for the energy of channel coefficient is

E[∣∣ξkβl,k

∣∣2]= e−τk/Γe−τl,kγr , (31)

where Γ is the cluster decay factor and γr is the ray decay factor.The parameters to generate a channel realization are given in Table 6 [116]. The

main difference of this model compared to other channel models discussed here is thatthis channel model only introduces multipath reflections in an empty room withouthumans and does not consider the impact of the body.

59

Table 6. Hentilä’s model.

Model parameter Operating room X-ray room Intensive care unit

Λ [1/ns] 0.04 0.05 0.09

λ [1/ns] 2 1.5 2

Γ [ns] 9 13.3 16

γr [ns] 8 10 5

σ1,σ2 [dB] 3.4 3.4 3.4

σX [dB] 1.5 1.5 1.5

The parameters in Table 6 are: σ1 is the standard deviation of the cluster lognormalfading term, σ2 is the standard deviation of the ray lognormal fading term, and σX is thestandard deviation of the lognormal shadowing term X for a total multipath realization.

4.4.2 Fort’s model

Fort’s model includes statistics measured taking into account diffraction around thetorso and reflection from ground [118]. The model was revised by including indoorreflections in [119]. The measurements were done in the frequency range from 3to 6 GHz, using a vector network analyzer to measure the S21–parameter betweentwo Skycross SMT-3TO10M antennas placed at various positions on a torso. Themeasurements to model indoor reflections were carried out in an indoor office havingthe dimensions of 3.7 by 6.1 by 2.8 meters. Fort et al. model the amplitude distributionwith the lognormal distribution. They also observed a high correlation between theamplitudes of adjacent channel bins.

Fort’s model combines a body area channel model with an indoor reflections model.In the body area model, the first step is to generate the vector x of Nb uncorrelated, zeromean, unit variance normal variables, where Nb is the number of channel bins. Thecorrelation and variances of channel bins are introduced by the covariance matrix C.The gain of the bin b expressed in dB is represented as [118]

Gb,dB = xchol(C)−M−PdB(d), (32)

where chol() is the Cholesky factorization, C is the Nb by Nb covariance matrix extractedfrom the measured correlation coefficients and variances, and M is the Nb elementvector of means for each bin, and PdB(d) is the pathloss at the distance d.

60

The measurements in an indoor office showed that a channel forms from severalclusters [119]. The first set of MPCs creating the first cluster is due to diffraction aroundthe body. After the first cluster multiple overlapping clusters also arrive due to reflectionsfrom the surrounding environment. The authors apply a dual slope model to clusterdecay to model different decaying speeds of the first and second cluster groups. Thedual slope model is defined as [119]

GdB(τl) =

Γ1τl +Gσ m, τl ≤ τbp

Γ1τbp +Γ2(τl− τbp

)+Gσ m, τl > τbp,

(33)

where GdB(τl) is the magnitude of the cluster arriving at excess delay τl expressed indB, τbp is the breakpoint delay, Γ1 is the cluster decay rate of the first group, Γ2 is thecluster decay rate of the second group, Gσ is the standard deviation around the averagetrend, and m is the unit mean, unit variance normally distributed random variable. Themultipath components within cluster decay with the slope γτk can be depicted as [119]

GdB(τl + τk) = GdB(τl)+ γτk , (34)

where τk is the path arrival time within a cluster and γτk is the decaying slope for themultipath components within a cluster. The cluster arrival time τl is modeled as theWeibull distributed, as [119]

p(τl |τl−1) =αW

βW

αW

(τl− τl−1)αW−1 e−

(τl−τl−1

βW

)αW

, τl−1 < τl < ∞, (35)

where αW is the shape parameter and βW is the scale parameter. Finally, the body areaand indoor reflection models are combined to generate a channel realization. Table 7provides the parameters to generate a channel for different receiver positions [119].

Table 7. Fort’s model.

Receiver position Γ1 [dB/ns] tbp [ns] Γ2 [dB/ns] Gσ [dB] γτk [dB/ns] αW βW

Front –0.15 40 –0.52 3.3 2.4 0.03 2.21

Side –0.19 40 –0.33 4.1 1.5 0.01 2.11

Back –0.11 – – 2.7 1.3 0.02 2.33

61

4.4.3 IEEE 802.15.6

The IEEE 802.15.6 channel models include scenarios from an implant-to-implant link toa body surface-to-external link [114]. Channel model 3 represents a body-to-body linkincluding several frequency bands from 13.5 MHz to an UWB band. A body-to-externallink is represented by channel model 4 comprising the 900 MHz, 2.4 GHz, and UWBfrequency bands.

UWB model

The background of the body-to-body channel model of the IEEE 802.15.6 is introducedin [120] and the body-to-external model in [121]. The measurements were carried out ina hospital room for the body-to-body model [120]. The applied frequency band wasfrom 3.0 GHz to 11.0 GHz. A receive antenna was placed in the middle of the torso, andthe location of a transmit antenna varies from the head to an ankle. The distance betweenthe antennas was from 176 mm to 984 mm. The S21-parameter was measured ten timesfor each location. The subject was laying down in a bed in a room of 5.0 m by 7.0 m.

The body-to-external channel measurements were carried out in an office room overthe frequency band 3.1–10.6 GHz by using a vector network analyzer [121]. A transmitantenna was fixed near to a wall, and the location of a receive antenna attached on thebody was varied. The direction of the body was also considered to model shadowing bya human body.

In both cases, a single cluster model where the path amplitude al is modeled by theexponential decay Γ with the Rician factor γ0 is assumed. In channel model 4 (CM4)[121], the model is given on a linear scale, but transforming it to the logarithmic scalegives the same model as in channel model 3 (CM3), as [120]

10log10 |al |2 =

0, l = 0

γ0 +10log10(exp(− tl

Γ

))+S, l 6= 0,

(36)

where S is the normal distribution with the zero-mean and standard deviation of σS. Thepath arrival time tl is modeled by the Poisson distribution [120]

p(tl |tl−1) = λ exp(−λ (tl− tl−1)) . (37)

The number of arrival paths L is modeled by the Poisson distribution as [120]

p(L) =LL exp(L)

L!, (38)

62

where L is the average number of L. The extracted parameters for the channel modelsare tabulated in Table 8.

Table 8. Parameters for the IEEE 802.15.6 CM3 and CM4.

Channel model Bodydirection

Γ [ns] γ0 [dB] σS λ [1/ns] L

CM3 – 59.7 –4.60 5.02 0.541 38.1

CM4 0◦ 44.6346 22.2 7.30 1.995 400

CM4 90◦ 54.2868 18.8 7.08 1.995 400

CM4 180◦ 53.4186 15.8 7.03 1.995 400

CM4 270◦ 83.9635 17.3 7.19 1.995 400

2.4 GHz narrowband model

The 2.4 GHz channel model is based on the measurements at the frequency of 2.45 GHz,originally introduced in [122]. Several transmit and receive antenna locations wereconsidered around and along the body, and the S21-parameters were measured using avector network analyzer. The measurements were carried out in an office room. Themodel considers a pathloss and flat small-scale fading.

The combined exponential-linear saturation model for the path loss (PdB) wasconcluded to have the best fit with the measurement data, given as [122]

PL(d) =−10log10

(P0e−m0d +P1

)+σPnP, (39)

where P0 is the average loss close to the antenna and depends on the type of an antenna,m0 is the average decay rate, P1 is the average attenuation of components, σP representsthe lognormal variance, and nP is the zero mean and unit variance Gaussian randomvariable.

The flat small-scale fading is represented by the Ricean distribution with the K-factorthat decreases as the path loss increases, modeled as [122]

KdB = K0−mKPdB +σKnK , (40)

where K0 is the fit with measurement data for the K-factor for low path loss, mK

represents the slope of the linear correlation between the path loss and K-factor, σK isthe lognormal variance, and nK is the zero mean and unit variance Gaussian randomvariable. The model parameters for the body-to-body link are given in Table 9 [122].

63

Table 9. Parameters for the IEEE 802.15.6 narrowband channel model.

Parameter P0 [dB] m0 [dB/cm] P1 [dB] σP [dB] K0 [dB] mk [dB] σk [dB]

Value –25.8 2.0 –71.3 3.6 30.6 0.43 3.4

4.4.4 Taparugssanagorn’s model

Taparugssanagorn et al. modeled a UWB WBAN channel in a hospital environment[123]. The measurement setup included a vector network analyzer, SkyCross SMT-3TO10M-A antennas, and measurement control software. The S21-parameters betweenthe transmit and receive antennas were measured using the frequency band from 3.1GHz to 10.0 GHz. Three different hospital environments were considered: a hospitalroom, a corridor, and an operating room. In these environments, two radio links weremeasured. The transmit antenna placed on the left wrist and the receive antenna locatedin the middle of the torso creates radio link A1 (body-to-body). For radio link A2(body-to-external), the receive antenna was placed on a pole and the transmit antennawas on the left side of the waist. In addition, standing and lying down postures weretaken into account.

A measured channel impulse response is modeled as a tapped delay line, as givenin (27). The measurements showed that the path amplitude al follows the two-regionexponential decaying function, where the first region is due to propagation around thebody and the second region is due to the surrounding environment, as [123]

10log10 |al |2 =

0, l = 0

γ01 +10log10

(exp(− tl

Γ1

))+S1, 1≤ l ≤ l1

Er∑

m=1

(γ02m +10log10

(exp(− tl

Γ2m

)))+S2m, l2 ≤ l ≤ L−1,

(41)

where Er is the number of echoes in the second region. The path arrival time tl followsthe Poisson distribution as in the IEEE 802.15.6 model in (37) but uses two regions asdiscussed above

p(t|tl−1) =

λ1 exp(−λ1(tl− tl−1)) , 1≤ l ≤ l1

λ2 exp(−λ2(tl− tl−1)) , l2 ≤ l ≤ L−1.(42)

The total number of arrival paths is also modeled by the Poisson distribution, as definedin (38). The parameters extracted from the measurements are given in Table 10 [123].

64

Table 10. Taparugssanagorn’s model.

Model parameters Radio link A1 Radio link A2

Standing Lying down Standing Lying down

γ01 [dB] –61 –64 –74 –65γ02m [dB] –91,–81,19,

–87,–6,–99–85 –83 –84

Γ1 [ns] 1.11 3.12 6.67 4.14Γ2 [ns] 30.30, 31.25, 2.44,

29.41, 4.55, 108.7032.26 31.25 29.41

σS1 [dB] 2.45 6.31 4.41 4.86σS2 [dB] 2.07, 2.21, 1.62,

1.44, 1.20, 0.913.50 2.80 2.79

1/λ1 3.717 4.764 8.000 6.0241/λ2 6.135 6.369 5.430 8.000L 324 324 323 323

4.4.5 Summary of the applied UWB WBAN channel models

For the body-to-body channel, three channel models were introduced: Fort’s model,IEEE 802.15.6 channel model 3, and Taparugssanagorn’s model. All of these modelsapplied a vector network analyzer and similar Skycross antennas to measure the S21-parameter between the transmit and receive antennas located around or along thebody. In addition, the dielectric separation, i.e., placing dielectric material between theantennas and the body, was taken into consideration in all three measurement campaigns.Hentilä’s model is different from the other models discussed here since it does not takeinto account the effect of the human body. In addition, it models a UWB channel inempty hospital rooms.

Fort’s model is based on the measurements carried out in the frequency band3.0–6.0 GHz varying the transmit and receive antenna locations on a standing subject.Three separate receiver positions were identified: front, back, and side of the body.A correlation between adjacent channel bins was also observed, which separates thismodel from the other two models. Fort et al. applied the double cluster model (alsoknown as dual slope) to model the path amplitude decaying.

In the IEEE 802.15.6 channel model 3 measurements, the receive antenna was fixedin the middle of the torso and the location of the transmit antenna was varied. It appliedthe widest bandwidth from 3.0 to 11.0 GHz. The channel model was assumed to be a

65

Table 11. RMS delay spread (τRMS) and decay factor (Γ1 ) for the studied channels.

Channel model Hentilä Fort Taparugssanagorn IEEE 802.15.6

Channel OR ICU XR Front Back Body-to-

body

Body-to-

external

Body-to-

body

Body-to-

external

Simulated τRMS [ns] 10.1 15.7 15.0 0.50 8.78 9.98 10.91 19.53 34.38Γ1 [ns] 9 16 13.3 28.95 39.48 3.12 4.14 59.7 44.65

single cluster model. The IEEE 802.15.6 channel model 4 measurements were carriedout in the office room over the frequency band 3.1–10.6 GHz. The transmit antennawas at a fixed position and the receive antenna was attached to the body. Several bodydirections were measured.

Taparugssanagorn’s model includes body-to-body and body-to-external links. It isbased on the measurement campaign performed in a real hospital environment withinthe 3.1–10.6 GHz frequency band, including in a hospital corridor, a hospital room, andan operating room. The antennas were placed in the middle of the torso and on the leftwrist. The standing and lying down postures were measured. The channel model was adouble cluster model.

Table 11 provides the two main parameters for the investigated channels to describetheir behavior, i.e., the root mean square (RMS) delay spread τRMS and the decayingfactor Γ1. The RMS delay spread defines a channel delay spread containing most ofthe channel power [104]. It can also be used to estimate the coherence bandwidthof a channel. The larger the spread, the smaller the coherence bandwidth leading tomore fades in the frequency domain [104]. The decaying factor is related to the pathamplitude decaying as e−t/Γ1 [124]. As it can be seen, the parameter values differ greatlybetween the channel models. The impact of the selection of the channel model onthe performance of the FM-UWB and DS-UWB systems is investigated in the originalpapers and summarized in Chapter 5. The channel realizations for the IEEE 802.15.6CM3, Fort’s and Taparugssanagorn’s body-to-body link channel models are illustratedin Figure 9.

66

0

0.5

1IEEE 802.15.6 CM3

0 10 20 30 40 50 60 70

0

0.5

1

Nor

mal

ized

am

plitu

de [V

]

Fort's

0 5 10 15 20 25 30

0

0.5

1Taparugssanagorn's

0 5 10 15 20 25 30

Time [ns]

Fig. 9. Realizations of three channel models.

67

5 Summary of the original publications

This section summarizes the author’s contribution to the original research results onthe performance of the WBAN PHY layers. The studied WBAN PHY layers are theultra wideband FM-UWB and the narrowband SmartBAN PHY. These two PHY layerswere selected because there were no previously published results on their performancein interfered WBAN channels. The research focused on the performance analysis byapplying the developed software simulators. The common theme throughout the originalpapers is to study the link level performance of the selected systems around the humanbody and from the body to an external device.

The performance measures used were BER and FER, and the target values were setto 10−5 for BER and 10−2 for FER. The FER value of 10−2 was used in [22] to definethe receiver sensitivity values for the FM-UWB. This leads approximately to the BERvalue of 10−5 for short packets. These target values were applied to study the neededEb/N0 or SIR levels.

Table 12 summarizes the studied systems. The studied UWB systems comprisetwo PHY layers, i.e. the FM-UWB and DS-UWB, whereas the SmartBAN PHY isthe narrowband system. The bit rates were adjusted according to the selected set ofmedical applications used in WBAN [2]. The frequency bands were chosen based onthe applied channel model or radio frequency spectrum regulations. In Papers I and II,the frequency ranges are limited by the channel models, whereas the European Unionconditions concerning on the use of UWB within the frequency band 6.0–8.5 GHzwithout implementing a low duty cycle (LDC) or DAA technique [39] is considered inPaper III. For the narrowband SmartBAN system, the 2.4 GHz ISM band was applied.

5.1 Ultra wideband PHY — FM-UWB and DS-UWB

Papers I, II and III present the analysis of the simulation results concerning the FM-UWBand DS-UWB systems utilizing the channel models introduced in section 4.4. Paper IVintroduces the simulation results and performance analysis concerning the FM-UWBin the interfered IEEE 802.15.6 channel model 3. The applications with the bit ratesgiven in Table 13 were considered during the study. Bit rate per channel concernsa body-to-body link where a WBAN node transmits information to a WBAN hub.Aggregated bit rate refers to a multiplexed bit rate from nodes via a hub to an external

69

Table 12. List of the studied WBAN PHY layers in the original publications.

Paper PHY layer Channel model Frequency band Link Bit rate [Mbps]

I FM-UWB andDS-UWB

Hentilä 3.1 to 6.0 GHz Body-to-external 0.18, 1.8 and 24

II FM-UWB andDS-UWB

Fort 3.0 to 5.0 GHz Body-to-body 0.14 and 0.48

III FM-UWB andDS-UWB

Taparugssanagornand IEEE802.15.6

6.0 to 8.5 GHz Body-to-body andBody-to-external

0.48 and 5.76

IV FM-UWB IEEE 802.15.6 6.240–6.739 GHz Body-to-body 0.250 for uncoded0.2025 for

BCH(63,51)

V SmartBAN IEEE 802.15.6 2.4 GHz Body-to-body 0.22, 0.44 and0.89

VI SmartBAN IEEE 802.15.6 2.4 GHz Body-to-body 0.22, 0.44 and0.89 for

BCH(127,113)0.15, 0.31 and

0.61 forBCH(36,22)

server, i.e., the bit rate for a body-to-external link. In addition to these, a high speed linkto transmit uncompressed x-ray images from a measurement device to an image viewerusing the bit rate of 24 Mbps was included. The performance of the FM-UWB wascompared with the reference DS-UWB system. The comparison is possible since bothsignals have equal power resulting in equal energy per transmitted bit Eb.

Table 13. Bit rates for the selected applications.

Application Bit rate per channel [kbps] Aggregated bit rate [Mbps]

Electrocardiography (ECG) [125] 15 0.18

High-resolution ECG [126] 480 5.76

Electromyography (EMG) [127] 140 1.8

For the FM-UWB, the modulation index of FSK was set to 1, as defined in [22],and the parameters for FM were adjusted to correspond with the frequency band of theDS-UWB. For the FM demodulator, the delay factor was selected to have a maximum

70

override for each studied bandwidth, except in Paper I where it was fixed to 3 for all thestudied bandwidths. Table 14 summarizes the parameters for the FM-UWB.

Table 14. Summary of the parameters used in the FM-UWB simulations.

Paper Carrierfrequency[GHz]

Rb [Mbps] RF Bandwidth (BRF) [GHz] PGFM [dB]

I 4.55 0.18 2.9, 2.0, 1.0, 0.5 39.1, 37.4, 34.4, 31.41.8 2.9, 2.0, 1.0, 0.5 29.1, 27.4, 24.4, 21.4

II 4.0 0.14 2.0, 1.5, 1.0 38.5, 37.3, 35.50.48 2.0, 1.5, 1.0 33.2, 31.9, 30.2

III 7.25 0.48 2.5, 2.0, 1.5, 1.0, 0.5 34.2, 33.2, 31.9, 30.2, 27.25.76 2.5, 2.0, 1.5, 1.0, 0.5 23.4, 22.3, 21.1, 19.4, 16.4

IV 6.4896 0.25 0.4992 30.0

Table 15 lists the parameters for the DS-UWB used in the simulations. The basebandpulse waveform was changed from the Gaussian derivative to the eigenpulse due tochallenges in fitting the Gaussian derivative to a desired frequency mask without filtering.The receiver utilizes the selective or partial rake receiver with either coherent MRC ornon-coherent SLC combining technique. The non-coherent combining uses the OOKmodulation because the polarity of a data bit is lost during the SLC combining.

Table 15. Summary of the parameters used in the DS-UWB simulations.

Paper Modulation Pulse Tp [ns] fc[GHz] Bandwidth[GHz]

Rb [Mbps](PG [dB])

Rake(fingers)

Combining

I BPAM,OOK

10thderivative

of theGaussianmonocycle

0.7 4.57 3.11 1.8 (29.0),24 (17.7)

s-rake (8) MRC,SLC

II BPAM,OOK

Eigen 2.0 4.0 2.0 0.14 (35.5),0.48 (30.2)

s-rake(1,2,3,5,7)

MRC,SLC

III BPAM,OOK

Eigen 1.5 7.25 2.5 0.48 (31.4),5.76 (20.6)

p-rake(1,3,10,15)

MRC,SLC

Paper I presents the analysis of the performance in a channel in the case of emptyhospital rooms (surgery operation, x-ray examination and intensive care unit) without

71

considering the impact of the human body. The simulation results show that theFM-UWB system performance does not saturate when the data rate is limited to 180kbps. When the data rate is ten-fold and the processing gain is one-tenth, it cannot meetthe target error rate values. Besides the processing gain decrement, the choice of the FMdemodulator index explains the performance decrease in lower bandwidths. By usingthe data rate of 1.8 Mbps, the DS-UWB outperforms the FM-UWB. The non-coherentDS-UWB is simpler but it needs 20 dB higher Eb/N0 than the coherent DS-UWB toobtain the BER level of 10−5. When the data rate increases, the performance differencedecreases between the coherent and non-coherent detection. With the data rate of 24Mbps, the non-coherent algorithm needs 7 dB higher Eb/N0 than the coherent algorithmsto obtain the BER level of 10−5.

The simulation results show the importance of selecting the appropriate delayfactor for the FM demodulator in the case of the FM-UWB. When using a small Nd

for the lowest studied bandwidths, the override of the demodulator decreases andit degrades the performance dramatically. This issue was studied more widely inPaper II. For example, when fc is 4.0 GHz and BRF is 1.0 GHz, Nd = {3,5,7} leads tothe O = {0.378,0.625,0.877}, respectively. This can be seen in the performance so thatit saturates to the BER level of 10−2 for O = 0.378 and the highest override providesmore than 5 dB better Eb/N0 than the second highest value at the BER level of 10−5.

Paper II presents and discusses the analysis with simulation in Fort’s channel model.In the front-to-front (FF) channel, the transmitter and receiver locates in the front of thebody, and there are strong first arrived MPCs. The front-to-back (FB) channel has nodirect line-of-sight (LOS) components and the channel is constructed by MPCs dueto reflections from the body and the surrounding environment. The baseband pulsewaveform was selected as the eigenpulse, as introduced in section 4.1.2. The use of theGaussian derivative is feasible when the available frequency band is not restricted. TheEU limits the use of the UWB to 6.0–8.5 GHz without using DAA or LDC techniques[39]. It is very challenging to find a Gaussian pulse to fit this spectrum. Therefore,the pulse waveform was designed by using the method introduced in section 4.1.2.An optimal number of rake fingers (Nr) was found for the DS-UWB utilizing a SRakereceiver. The number of rake fingers plays an important role in terms of performanceand degree of complexity: too many fingers increases complexity and too few fingerscannot perform well. In addition, the channel profile affects the optimal Nr.

The results show that the coherent DS-UWB needs at least three fingers and the non-coherent DS-UWB five fingers to attain a reasonable BER performance in the FF and

72

FB channels. The FM-UWB outperformed the non-coherent DS-UWB in both channels,and the coherent one-finger DS-UWB in the FB channel, as shown in Figure 10. The

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44Eb/N0 [dB]

10-5

10-4

10-3

10-2

10-1

100Bi

tErro

rRat

e

DS, coherent, 1 fingerDS, coherent, 3 fingersDS, non-coherent, 1 fingerDS, non-coherent, 5 fingersFM, BRF = 2.0 GHz

FM, BRF = 1.5 GHz

FM, BRF = 1.0 GHz

Fig. 10. FM-UWB and DS-UWB with RRRbbb === 140 kbps in Fort’s FB channel (Paper II © 2010Inderscience Publisher).

performance of the DS-UWB can be improved at the cost of complexity by increasingthe number of fingers and introducing coherent detection. The simulation results clearlypoint, as expected, that the coherent three-finger DS-UWB has the best performance ofthe studied receivers. They also show that Nd of the FM-UWB plays an important role interms of the system performance. The bandwidth of 1.5 GHz with Nd = 5 achieves thebest efficiency of the FM demodulators, and thus the best overall performance amongthe other studied parameters.

Paper III presents the performance analysis of the systems determined in Taparugs-sanagorn’s and IEEE 802.15.6 channel models. The differences between these channelmodels are discussed in [128]. As shown in Table 11, the main parameters characterizingthe channel models differ from each other. The simulated RMS delay spreads for theIEEE 802.15.6 links are two or three times larger than in Taparugssanagorn’s model,and the difference in the decaying factor of the first cluster is even higher. A longchannel delay spread disrupts the performance of the FM-UWB even with a lower datarate, as shown in Figure 11 where CWC1 and IEEE1 refer to body-to-body links. The

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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40Eb/N0 [dB]

10-5

10-4

10-3

10-2

10-1

100

BitE

rrorR

ate

BRF = 2.5 GHz

BRF = 2.0 GHz

BRF = 1.5 GHz

BRF = 1.0 GHz

BRF = 0.5 GHz

CWC1

IEEE1

IEEE1 = Solid lineCWC1 = Dashedline

Fig. 11. FM-UWB with RRRbbb === 480 kbps in Taparugssanagorn’s (CWC1) and IEEE 802.15.6(IEEE1) channel models (Paper III © 2010 IEEE).

DS-UWB utilized a PRake with the coherent or non-coherent detection. In both cases,the overall performance of the DS-UWB is better than the FM-UWB with the used datarates. The results indicate that the first MPC in Taparugssanagorn’s model is not verystrong. The performance of the one-finger coherent detection saturates to the BERlevel of 0.1, although it performs well in the IEEE 802.15.6 model. The data rate forthe body-to-external link is 5.76 Mbps, which is too high for the FM-UWB due to theprocessing gain loss.

The analysis of the FM-UWB performance in the interfered IEEE 802.15.6 body-to-body channel model is provided in Paper IV. The simulation parameters are now fixedto follow the parameters defined in the IEEE 802.15.6 standard [22]. The uncoded datarate is 250 kbps and the FM-UWB system is operating over channel number 3. TheBCH code is also applied. The interference is modeled as CGN, representing IEEE802.15.4 interference operating in the same band. The overall simulation results indicatethat in-band interference has no notable impact on the uncoded or coded FM-UWBsystem performance when the SIR level is 0 dB or more. By decreasing the SIR levelbelow 0 dB, the performance starts to degrade and finally saturates to the FER level of1.1×10−2 when SIR is −5 dB for the uncoded system or to the FER level of 3×10−2

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when SIR is −8 dB for the coded system. Figure 12 presents the simulation results forthe coded FM-UWB.

0 5 10 15 20 25 30 35

Eb/N0 [dB]

10-3

10-2

10-1

100F

ram

e er

ror

rate

no intf.SIR=0SIR=-3SIR=-7SIR=-8SIR=-6SIR=-9

Fig. 12. Frame error rate of the coded FM-UWB in the IEEE 802.15.6 channel model (Paper IV© 2017 ACM).

5.2 Narrowband PHY — ETSI SmartBAN

The simulation results concerning the AWGN channel were applied to define therequirement for the receiver sensitivity, similarly as presented in section 8.9.1 of theIEEE 802.15.6 standard [22]. The same parameters are applied, i.e., the noise figure of13 dB, implemention loss of 6 dB, and the PSDU of 255 octets. The sensitivity valuesare given in Table 16. The numbers are better for SmartBAN than IEEE 802.15.6.

The performance of the narrowband SmartBAN system was studied in an interferedIEEE 802.15.6 channel model in the 2.4 GHz band. The interference model is basedon the spectrum occupancy measurements performed at the Oulu University Hospital.Three scenarios were defined based on the channel occupancy: low, moderate, and high.

Paper V is based on the work done in the ETSI STF511. These results were exploitedin the standardization of SmartBAN. The simulation results concerning the AWGNchannel provided the receiver sensitivity numbers for SmartBAN. The Eb/N0 thresholdvalues to gain FER of 1% and 10% for various frame sizes and the PPDU repetitionin the IEEE 802.15.6 channel are provided in Appendix 1. As it is expected, the

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retransmission and PPDU repetition methods provide a better performance in a fadingchannel at the cost of data rate.

The results show that the SmartBAN system needs the 4-time PPDU repetition anda high SIR to overcome the interference with high channel occupancy. In the case oflow interference, the results are more promising, and the FER level of 1% is achievedwithout the PPDU repetition for SIR of 6 dB or more. Figure 13 illustrates the FERperformance of SmartBAN with the 1-time PPDU repetition.

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36

Eb/N

0 [dB]

10-3

10-2

10-1

100

Fra

me

erro

r ra

te

SIR=-3 dB, lowSIR=-3 dB, highSIR=0 dB, lowSIR=0 dB, highSIR=3 dB, lowSIR=3 dB, highSIR=6 dB, lowSIR=6 dB, highSIR=9 dB, lowSIR=9 dB, high

Fig. 13. Frame error rate of the SmartBAN system in the interfered channel, 1-time PPDUrepetition (Paper V © 2017 IEEE).

Table 16. Receiver sensitivity numbers for SmartBAN (Paper V © 2016 ETSI).

Symbol rate[MSps]

Code rate Repetition Information rate[Mbps]

Eb/N0, FER =10%

Max. input levelat sensitivity

[dBm]

1.0 1 1 1.0 8.8 –86.21.0 1 2 0.5 5.8 –92.21.0 1 4 0.25 2.8 –98.21.0 113/127 1 0.89 7.4 –88.11.0 113/127 2 0.44 4.3 –94.31.0 113/127 4 0.22 1.4 –100.2

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In Paper VI, the use of a better coding rate of BCH was studied to gain betterperformance in the interfered channel. The applied coding rate (36,22) was defined to beused for the SmartBAN PLCP header. It is a shortened version from the BCH(127,113)code. The simulation results for the coding rates (127,113) and (36,22), using the 2-timePPDU repetition in the high interference channel, are depicted in Figure 14. As canbe seen from the results, the better coding rate (36,22) provides the FER performanceof 1% with SIR of 3 dB, whereas the lower coding rate (127,113) needs 9 dB to gainthat level. Nevertheless, both coding rates need higher PPDU repetitions to provide theneeded performance with lower SIR values.

0 5 10 15 20 25 30 35

Eb/N

0 [dB]

10-3

10-2

10-1

100

Fra

me

erro

r ra

te

BCH(36,22), SIR=9 dBBCH(36,22), SIR=6 dBBCH(36,22), SIR=3 dBBCH(36,22), SIR=0 dBBCH(127,113), SIR=9 dBBCH(127,113), SIR=6 dBBCH(127,113), SIR=3 dBBCH(127,113), SIR=0 dB

Fig. 14. Frame error rate of the SmartBAN system, 2-time PPDU repetition, high interferencescenario (Paper VI © 2017 IEEE).

5.3 Summary

This chapter presented the summary of the author’s contribution published in threeconferences and two journal articles with one submitted conference manuscript. Thestudy was divided into two approaches, the FM-UWB representing the UWB PHY andthe SmartBAN PHY representing a narrowband system. Since the FM-UWB systemcan be seen as an analog implemention of a spread spectrum system, it is feasible to usethe DS-UWB as the reference system for the FM-UWB.

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For the FM-UWB receiver, two parameters determining the performance can bedefined: the receiver processing gain and the override of the FM demodulator. As it wasshown, the optimal selection of these parameters introduced the best performance. TheFM-UWB system is designed for low (1–100 kbps) and medium (100–1000 kbps) bitrate applications [70].

The results indicate out that the system can achieve the bit rate of hundreds of kbpsin a dense multipath environment to meet the target performance. When the receiverparameters and channel characteristics are favorable for the FM-UWB, it performsbetter than the coherent one-finger DS-UWB. It means that the processing gain is largeenough, Nd and the system bandwidth are optimized, and the channel does not havea long RMS delay spread. When using the bit rate and BCH code as defined in theIEEE 802.15.6 standard, the results indicate that the FM-UWB is resistant to in-bandinterference. The simulation results and simple implementation of the FM-UWB makessuitable for low data rate health monitoring applications for which it was designed.

The author’s contribution to the SmartBAN system study included the definition ofthe receiver sensitivity, and the simulation results concerning the fading channel andinterference. The nominal bit rate for SmartBAN is 1 Mbps [32]. The results indicatethat this bit rate can be achieved in a fading channel. When interference is introduced,the system needs a diversity method concerning the PPDU repetition to mitigate theimpact of interference. Use of higher code rate improves the performance at the cost ofbit rate. It can be concluded that the achievable bit rate in an interfered channel is nearto 250 kbps, as was determined for the FM-UWB. On the other hand, the SmartBANsystem needs a higher SIR level than the FM-UWB to achieve the FER performance of1%.

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6 Conclusion

This thesis focused on the analysis of the link level performance of the FM-UWBand SmartBAN PHY layers in the presence of interference. The FM-UWB system isspecified in the IEEE 802.15.6 standard, whereas the narrowband ETSI SmartBANsystem is a new specification for ultra-low power WBANs. The literature review pointedout the lack of performance analysis for both systems in interfered WBAN channels.

In Chapter 2, considerations of WBAN communications were discussed. There areseveral design considerations, radio frequency spectrum regulations, transmission powerlimitations due to SAR restrictions, and functional requirements to be met. There arealso other radio technologies proposed for WBANs, such as ZigBee, Bluetooth LE,IEEE 802.15.4, and proprietary solutions such as ANT, Insteon, and Z-Wave [5, 8, 9].ZigBee and Bluetooth are mainly intended for small and inexpensive devices such aswireless sensors. ANT is a sensor network technology for sports, fitness and healthproducts. Insteon and Z-Wave are mesh technologies designed for home automation. Asdiscussed in Chapter 2, WBANs need a dedicated standard due to the requirements anddesign considerations that are unique to WBANs.

Chapter 3 introduced the IEEE 802.15.6 and SmartBAN standards, and analyzedthe strengths and weaknesses of both standards. In addition, the previous studies onFM-UWB and SmartBAN were reviewed.

The framework of the research was presented in Chapter 4, and the signal models,channel models and, interference models were introduced. The summary of thepublications was given in Chapter 5. Papers I, II, III and IV provided the simulationresults concerning the FM-UWB in several WBAN channel models. The DS-UWBsystem was applied as the reference system. Papers V and VI presented the novelperformance results of the SmartBAN system in the IEEE 802.15.6 channel model. Inaddition to the simulation results, the receiver sensitivity numbers were introduced.

6.1 Main findings and discussion

It is important to select the appropriate parameter values for the FM-UWB system.The receiver processing gain and demodulator sensitivity proportional to Nd play animportant role in the performance. If they are selected appropriately, the simulationresults concerning the studied channel models show that the FM-UWB is a good

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candidate for applications with a data rate up to hundreds of kbps. The tranmission powerof UWB communications is limited by the power spectral density of −41.3 dBm/MHz.This is clearly below the limit bounded by SAR. Despite the low transmission power,the reasonable range of 2.0 meters can be reached for on-body communications, asshown in the link budget calculation in Appendix 2.

The results concerning in-band interference show that the FM-UWB is tolerant tointerference. The uncoded FM-UWB can reach the FER level of 1% with SIR of −4 dBand the FER level of 10% with SIR of −5 dB. Coding improves the performance by3 dB in SIR.

The results show that SmartBAN performs well in the studied IEEE 802.15.6 channelwithout interference. A similar link budget calculation as performed on the FM-UWBwould indicate that the communication range of the SmartBAN system is more thanenough for WBAN on-body communications due to the lower propagation losses andhigher transmission power. The performance analysis indicates that the SmartBANsystem needs SIR of 6 dB without the PPDU repetition to gain the FER level of 1%in the low interference scenario. For the high interference scenario, 4-time PPDUrepetition and SIR of 9 dB are needed. When relaxing the FER requirement to 10%,it allows SIR of 3 dB and 1-time PPDU repetition in both interference scenarios. Abetter coding scheme improves the SmartBAN performance, as expected. In bothinterference scenarios, the FER level of 1% can be gained with 1-time PPDU repetition.The performance gain resulting from the PPDU repetition and higher coding rate comesat the cost of information rate.

In Table 17, the recommendation for choosing the appropriate PHY between theFM-UWB and SmartBAN PHY for on-body WBAN communications is provided onthe basis of the simulation results. The FM-UWB performed well with a low SIR.SmartBAN has an option to use several PPDU repetitions to improve its performance.This reduces the information rate, but even with 4-time PPDU repetition, it is equal tothe IEEE 802.15.6 FM-UWB rate. This makes it a good choice for high SIR conditions.For low SIR conditions and low FER requirements, both systems are good candidates.The SmartBAN system needs better coding and a maximum number of PPDU repetitionsto perform SIR below 0 dB.

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Table 17. Guideline for PHY selection between the FM-UWB and SmartBAN.

Demands SIR condition

Low (< 0 dB) High (> 0 dB)

FER requirement Low (< 10%) FM-UWB or SmartBAN SmartBANHigh (< 1%) FM-UWB SmartBAN

How should the SIR values be interpreted? If considering mutual interference withsimilar parameters and free-space path loss, SIR of 6 dB doubles the communicationdistance in a desired WBAN to an interferer. For example, a distance of two metersbetween a hub and node, requires a distance of four meters between the hub and theinterferer with SIR of 6 dB. In other words, by increasing the transmission power of theinterferer by 6 dB, the separation must be doubled to have equal SIR. For example,the maximum EIRP of WiFi is limited to 36 dBm by the FCC [49]. As discussed inChapter 2, SAR limits the maximum EIRP of on-body WBAN to 2 dBm, which meansthat WiFi can use approximately 2,500 times (34 dBm) stronger transmission powerthan a WBAN. In addition to the existing wireless technologies, the number of wearabledevices is also growing strongly, especially in the medical application segment. It meansthat the coexistence issue needs to be considered not only by introducing techniquesenhancing performance but also by adopting interference mitigation techniques.

The research work presented in the thesis has some limitations. The selectedperformance measures, BER and FER, indicate an average error performance of asystem. Other measures, such as the outage probability, indicates how reliable a link is.This is an important measure for high reliability applications to show link availability.For the FM-UWB, the interference source is modeled as CGN. A more realistic modelfor interference could be applied.

6.2 Future work

Due to the operation in the 2.4 GHz ISM or UWB frequency band, it is necessary toconsider the coexistence issues concerning WBANs. Interference has serious impactson the FM-UWB and SmartBAN system performances. In some conditions, the impactis strong enough to make the communications link unrealiable, which is not acceptablein medical applications. Interference sources can be divided into mutual interferencefrom multiple and adjacent WBANs and cross interference from other systems [25].

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For the SmartBAN system, ZigBee, Bluetooth LE, and WiFi are operating in the samefrequency band, whereas the IEEE 802.15.4 UWB shares its bands with the IEEE802.15.6 FM-UWB system.

The SmartBAN study introduced in this thesis was performed for ETSI to supportit in the standardization process. The work could be continued by studying possibleinterference mitigation techniques. The very first interference mitigation techniquecould follow a cognitive approach that applies a scan-and-select mechanism, i.e., thehub would periodically scan the frequency band to decide the communications channel.For mutual interference, if a WBAN is operating in a closed environment, such as ahospital, a cognitive radio network could be a feasible choice. In this approach, thecontroller could manage the communications of WBANs and other local systems suchas WiFi. Viewpoints related to the cognitive radio are discussed in [129].

In addition to the study of interference mitigation techniques, a more detailed com-parison between the IEEE 802.15.6 narrowband and SmartBAN should be performed.

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Appendix 1 SmartBAN threshold values

Table 18. EEEbbb///NNN000 values for FER of 10% and 1% in the IEEE 802.15.6 channel (Paper V © 2016ETSI).

FER 10% 1%

Retransmission Frame size [octets] PPDUrepetition

w/o Retx w/ Retx w/o Retx w/ Retx

501 16 9.4 26.3 15.32 8.8 5.0 13.9 8.64 3.8 1.5 7.3 3.7

2501 17.7 9.8 27.8 16.22 9.1 5.4 15.0 8.74 4.2 2.4 7.2 4.1

5001 17.7 10.2 28.2 16.42 9.5 6.4 15.1 9.44 5.3 2.6 7.6 5.3

10001 17.8 10.4 28.3 16.92 9.6 6.4 15.9 9.6

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Appendix 2 Link budget

The link budget for the FM-UWB is given in Table 19. The calculation follows thetypical link budget approach as given, for example, in [130]. For the FM-UWB, theSNR conversion from the subcarrier to RF is done according to [70] as

SNRSUB =BRF

BSUB

SNR2RF

(1

1+4SNRRF

), (43)

where SNRSUB is the subcarrier SNR, SNRRF is the RF SNR, BRF is the RF bandwidth,and BSUB is the subcarrier bandwidth. The body surface-to-body surface channel lossparameters are given in [114]. The reference parameter values for the receiver losses andnoise figure as defined in [22] are applied. The transmitter power equals to −14.3 dBmfor the channel bandwidth of 499.2 MHz.

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Table 19. Link budget for FM-UWB.

Parameter Value Unit Remarks

System parameters

Channel bandwidth (A) 499.2 MHz IEEE 802.15.6 standard

Information rate (B) 202 kbps IEEE 802.15.6 standard

FSK modulation index (C) 1

Subcarrier bandwidth (D) 404 kHz D = B(C+1)

Transmitter

Transmission power (E) –14.3 dBm –41.3 dBm/MHz over A

Antenna gain (F) 0.0 dBi Isotropic antenna

EIRP (G) –14.3 dBm G = E+F

Receiver

Thermal noise power (H) –87.0 dBm

Receiver noise figure (I) 5.0 dB Low-noise amplifier [70]

Receiver noise floor (J) –82.0 dBm J = H+I

Required Eb/N0 15.0 dB Fig. 12

Required SNRRF (K) –8.5 dB Eq. (43)

Receiver sensitivity (L) –90.5 dBm L = J+K

Receiver antenna gain (M) 0 dBi Isotropic antenna

Receiver losses (N) 5.0 dB IEEE 802.15.6 standard

Minimum signal reception strength (O) –85.5 dB O = L-M+N

Channel

Fade margin (P) 4.4 dB IEEE 802.15.6 channel model

Maximum channel loss (Q) 66.8 dB Q= G-O-P

Maximum reachable distance 2.0 m IEEE 802.15.6 channel model

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Original publications

I Viittala H, Hämäläinen M & Iinatti J (2008) Suitability Study of DS-UWB and UWB-FMfor Medical Applications. In: The 11th International Symposium on Wireless PersonalMultimedia Communications, pp. 1–5.

II Viittala H, Nahar BN, Hämäläinen M & Iinatti J (2010) Medical Applications AdaptingUltra Wideband: A System Study. International Journal of Ultra Wideband Communicationsand Systems 1(4): 237–247, DOI: 10.1504/IJUWBCS.2010.034305.

III Viittala H, Hämäläinen M & Iinatti J (2010) Impact of Difference in WBAN Channel Modelson UWB System Performance. In: IEEE 11th International Symposium on Spread SpectrumTechniques and Applications, pp. 175–180, DOI: 10.1109/ISSSTA.2010.5651033.

IV Viittala H, Hämäläinen M & Iinatti J (2017) Link-level Performance of FM-UWB inthe Interfered IEEE 802.15.6 Channel. In: 12th International Conference on Body AreaNetworks, pp. 1–4.

V Viittala H, Mucchi L, Hämäläinen M & Paso T (2017) ETSI SmartBAN System Performanceand Coexistence Verification for Healthcare. IEEE Access, Vol 5: 8175–8182, DOI:10.1109/ACCESS.2017.2697502.

VI Viittala H, Mucchi L & Hämäläinen M (2017) Performance of the ETSI SmartBAN Systemin the Interfered IEEE 802.15.6 Channel. In: 11th International Symposium on Medical In-formation and Communication Technology, pp. 1–4, DOI: 10.1109/ISMICT.2017.7891757.

Reprinted with permission from WPMC (I), Inderscience Publisher (II), ETSI (V), IEEE(III,V,VI) and ACM (IV).

Original publications are not included in the electronic version of the dissertation.

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