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TRANSCRIPT
UNIVERSITA’ DEGLI STUDI DI PAVIA FACOLTA’ DI INGEGNERIA
Dottorato di Ricerca in Ingegneria Elettronica, Informatica ed Elettrica XXIII Ciclo
Orthogonal Frequency Division Multiplexing Systems and Application in Cognitive Radio Networks
Ph.D. Thesis of Anna Vizziello
Advisor Prof. Lorenzo Favalli
Academic Year 2009/2010
Contents
Introduction 1
Part I: OFDM Systems and Inter Carrier Interference Mitigati on 6
1 Orthogonal Frequency Division Multiplexing Systems 7
1.1 Wireless Propagation . . . . . . . . . . . . . . . . . . . . . . . 7
1.2 Principles of OFDM . . . . . . . . . . . . . . . . . . . . . . . 13
1.2.1 Orthogonality . . . . . . . . . . . . . . . . . . . . . . . 14
1.2.2 Cyclic Prefix, Inter-Symbol and Inter-Carrier Interference 16
2 Mitigation of Intercarrier Interference in OFDM Systems 19
2.1 System model . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.1.1 Transmission model . . . . . . . . . . . . . . . . . . . 22
2.1.2 Frequency Channel Matrix approximation . . . . . . . . 23
2.2 Proposed algorithm for ICI Mitigation . . . . . . . . . . . . . . 24
2.2.1 Generalized EM algorithm for joint ICI estimation and
equalization . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.2 Implementation of the Proposed Algorithm . . . . . . . 26
2.2.3 Simplified Algorithm . . . . . . . . . . . . . . . . . . . 29
2.3 Simulation results and comments . . . . . . . . . . . . . . . . . 29
Part II: Application of OFDM to Cognitive Radio Networks 36
I
3 Cognitive Radio Networks 37
3.1 Cognitive Radio . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.1.1 Physical Architecture of the Cognitive Radio . . . . . . 42
3.1.2 Cognitive Radio Network Architecture . . . . . . . . . 44
3.1.3 Spectrum Management Functions . . . . . . . . . . . . 48
3.2 Spectrum Sensing . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2.1 PU Activity Models . . . . . . . . . . . . . . . . . . . 52
3.2.2 PU Detection . . . . . . . . . . . . . . . . . . . . . . . 53
3.3 Spectrum Decision . . . . . . . . . . . . . . . . . . . . . . . . 60
3.4 Spectrum Sharing . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.4.1 Overview of Spectrum Sharing Techniques . . . . . . . 62
3.4.2 Overview of MAC Protocols . . . . . . . . . . . . . . . 63
3.5 Spectrum Mobility . . . . . . . . . . . . . . . . . . . . . . . . 65
3.6 Routing Layer in Cognitive Radio Networks . . . . . . . . . . . 67
3.6.1 Factors influencing routing protocols design . . . . . . .68
3.6.2 Overview of the classification of routing protocols . .. 69
3.7 Transport Layer in Cognitive Radio Networks . . . . . . . . . . 70
3.8 Standards for Cognitive Radio Networks . . . . . . . . . . . . . 71
4 OFDM Signals Recognition in Cognitive Radio Networks 73
4.1 Motivation for OFDM Signals Recognition . . . . . . . . . . . 74
4.2 System Architecture and Modules . . . . . . . . . . . . . . . . 76
4.3 Primary Users Type Characterization . . . . . . . . . . . . . . . 78
4.3.1 Primary Users Activity Model . . . . . . . . . . . . . . 78
4.3.2 Cyclostationary Feature Detector/Classifier . . . . . . . 79
4.3.3 PU Characteristics Module . . . . . . . . . . . . . . . . 82
4.4 Adaptability Effects on Cognitive Radio . . . . . . . . . . . . . 84
4.4.1 CR Adaptive Parameters . . . . . . . . . . . . . . . . . 84
4.4.2 CR Throughput/Interference Adapter . . . . . . . . . . 86
4.5 Performance evaluation and comments . . . . . . . . . . . . . . 89
II
4.5.1 Simulation Environment . . . . . . . . . . . . . . . . . 89
4.5.2 CAF Detector/Classifier . . . . . . . . . . . . . . . . . 89
4.5.3 CR Adaptive Throughput . . . . . . . . . . . . . . . . . 91
4.5.4 Main comments . . . . . . . . . . . . . . . . . . . . . . 93
5 Radio Resource Management in Cognitive Radio Networks 94
5.1 Proposed System Architecture . . . . . . . . . . . . . . . . . . 97
5.1.1 PU Type Features Extraction . . . . . . . . . . . . . . . 98
5.1.2 Available Capacity Calculation . . . . . . . . . . . . . . 100
5.2 Optimization Framework . . . . . . . . . . . . . . . . . . . . . 101
5.3 Proposed Suboptimal Solution . . . . . . . . . . . . . . . . . . 105
5.3.1 Resource Allocation inside a single cluster . . . . . . . 108
5.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . 110
5.4.1 Simulation Environment . . . . . . . . . . . . . . . . . 110
5.4.2 CRs achieved data rates and satisfaction . . . . . . . . . 111
Conclusions 114
Bibliography 115
Appendix 127
A List of Acronyms 127
III
Introduction 1
Introduction
In recent years there has been an increasing demand for high data rate wire-
less applications. Robust high data rate communications face several challenges.
Specifically, transmission of high rate information typically experiences high de-
lay spread in wireless environments.
Orthogonal Frequency Division Multiplexing (OFDM) is a transmission tech-
nique with high robustness in environments with large delayspread. In partic-
ular, an OFDM system is based on the idea of dividing the available bandwidth
into narrow sub-channels and sending low data rate signals in parallel on them.
The total high data rate is assured by the parallel transmissions. In this way, the
duration of symbol time becomes longer then in a Single Carrier (SC) scheme,
and OFDM turns out to be more robust against Inter-Symbol Interference (ISI)
caused by delay spread than SC scheme.
However, performance of OFDM systems is affected by channelestimation,
mobility and frequency offset at the local oscillator (LO).Channel estimation is a
well known issue to be solved to have an acceptable receptionquality. Moreover,
mobility introduces time-variations, which can make the link less reliable. Due
to the expansion of symbol length, OFDM systems are sensitive to time varia-
tions. In fact, time variations may introduce Inter-Carrier-Interference (ICI). Fur-
thermore, frequency offset at the local oscillator introduces ICI as well, which
further degrades the performance. Therefore to ensure reliable communication,
designing a robust channel estimation and ICI mitigation becomes essential.
In the first part of this thesis, the principles and the features of OFDM with
Introduction 2
a solution for channel estimation and inter-carrier interference mitigation are
presented. The proposed algorithm is explained referring to the Expectation-
Maximization (EM) paradigm.
Despite the OFDM sensitivity to ICI, OFDM is robust in high delay spread
caused by multipath environment and eliminates the needs ofequalizing the ef-
fect of delay spread. This feature allows high data rates andhas resulted in the
selection of OFDM as a standard for Digital Audio Broadcasting (DAB) [1], Dig-
ital Video Broadcasting (DVB) [2], Wireless Fidelity (WiFi),Worldwide Inter-
operability for Microwave Access (WiMax), and the 3rd Generation Partnership
Project (3GPP) Long Term Evolution (LTE). Moreover, it is being considered
as a potential technology for the promising Cognitive Radio (CR)Networks.
Specifically, cognitive radio technology is proposed for opportunistic spectrum
access as one of the enabling technologies for an efficient radio spectrum utiliza-
tion.
In the second part of the thesis, the application of OFDM to cognitive radio
networks is developed. In particular, specific features of OFDM signals are ex-
ploited to handle some issues in cognitive radio networks. Bydefinition, a CR
is capable to change its transmitter parameters and interacting with the environ-
ment. In more details, a CR can utilize the band, licensed to specific services, on
the condition that it has to vacate the spectrum as soon as thePrimary User (PU)
is detected. To this purpose, several sensing techniques are commonly used to
detect PU signals. It has to be noticed that OFDM signals exhibit periodicities
embedded in equally spaced sinusoidal carriers, cyclic prefix (CP) and pilot pat-
terns that can be exploited not only to detect but also to classify heterogeneous
PUs. Following this reasoning, a better knowledge of the environment is avail-
able. We also exploit the ability of classifying heterogeneous PUs to improve the
CR adaptability in terms of throughput and interference protection towards PUs,
and to design a flexible Cognitive Radio Resource Management (RRM).
Introduction 3
Dissertation outline
This research is devoted to the analysis of OFDM systems and application to
cognitive radio networks. The work consists in two parts: inthe first part, the
OFDM technique is presented highlighting the OFDM sensitivity to inter-carrier
interference. The proposed solution to mitigate the interference among subcar-
riers is then described. In the second part, the OFDM application to cognitive
radio networks is introduced, showing how to exploit the specific features of
OFDM signals to deal with some issues in cognitive radio networks.
The first part of the thesis, dealing with OFDM Systems and ICI mitigation,
consists inChapter 1, andChapter 2.
Chapter 1describes the principles and features of OFDM Systems. First,
a brief overview on wireless propagation is given. Then, theOFDM technique
is analyzed highlighting benefits and drawbacks. In particular, we focus on the
robustness of OFDM to multipath channels and its sensitivity to the inter-carrier
interference.
Chapter 2analyzes the effects of ICI in more details and proposes a solution
for ICI Mitigation. In particular, it is possible to represent this interference in
the frequency domain by means of an ICI matrix, whose estimation is crucial
to ensure reliable communication. Specifically, we proposean iterative method
to mitigate the interference among subcarriers exploitingthe presence of pilot
tones in the frequency domain. We describe the proposed method considering
the EM paradigm. The proposed technique is very effective inmultipath slow
fading channels with frequency offset at local oscillator and a simplified version
of it is also introduced in case of AWGN channel with frequencyoffset at the
local oscillator. Simulation results show that the presented algorithm converges
very quickly and looks promising to be used in actual implementations.
Introduction 4
The second part of the thesis, dealing with the application of OFDM to cog-
nitive radio network, consists inChapter 3, Chapter 4, andChapter 5.
Chapter 3presents an overview of cognitive radio networks. First, webriefly
describe the cognitive radio technology. Then, the physical architecture of CR
equipments and the network architectures on licensed band and on unlicensed
band are presented. The spectrum management framework is also analyzed.
The functionalities of the spectrum management are explained in more details,
i.e. spectrum sensing, spectrum decision, spectrum sharing, spectrum mobility.
More emphasis is given to spectrum sensing that is at the basis of the following
Chapter 4. We also briefly investigate how CR features influence the perfor-
mance of the upper layer protocols, i.e., Medium Access Control (MAC), rout-
ing, and transport, respectively. Finally, the current effort on CR standardization
are summarized.
Chapter 4shows how we exploit OFDM features in CR networks. The fea-
tures of OFDM signals have been employed to recognize heterogeneous PUs in
order to improve CR adaptability. Moreover, the ability of classify heterogeneous
PUs has been used to adapt CR transmission parameters to the environment in
the most efficient way. In fact, after classifying OFDM PU signals, the charac-
teristics of each PU type, the allowed interference level, the bandwidth and the
idle time, are extracted and exploited for CR adaptability effects. According to
this, a new CR throughput/interference adapter is proposed.The CR throughput
is efficiently increased depending on the specific features of PU types.
Chapter 5proposes an efficient Cognitive Radio Resource Management (CRRM),
which exploits the ability of classifying heterogeneous PUs. In this context, the
RRM calculates the available capacity of CR network based on thedifferent
PU types identified by CRs. In particular, we propose an RRM control mecha-
Introduction 5
nism to regulate the sharing of total available capacity among CRs. Specifically,
the existence of a specific PU type in a CR network influences theamount of
available capacity for the CRs. A cluster of CRs that share the same available
capacity is formulated based on the influencing PU type(s). As a result, the pro-
posed RRM identify different PU types with their associated CR clusters. The
proposed RRM is comprised of two stages: first, the RRM assigns CRs totheir
appropriate clusters based on CRs demands and available capacity in the cluster
(Admission Control Policy); then, the RRM allocates the required resources for
the newly assigned CRs to the cluster based on Orthogonal Frequency Division
Multiplexing Access (OFDMA) technique.
An Optimization framework for cognitive RRM that exploits multiple fea-
tures of heterogeneous PUs is proposed. The objective of theoptimization frame-
work is to minimize the difference between the available capacity and the achiev-
able data rate while satisfying CR demands and interference constraints. A Sub-
optimal solution for cognitive RRM that requires feasible computational require-
ments is also proposed.
Finally, Conclusions and future worksare discussed by showing some direc-
tions to improve the proposed solutions.
Conclusions 114
Conclusions
OFDM is a popular scheme for wideband digital communication, used in several
applications such as DAB, DVB, WiFi, WiMax, and LTE. Moreover,OFDM
is being considered as a potential technology for the promising cognitive radio
networks. In this thesis, the inter-carrier interference effects on OFDM are dis-
cussed.
In the first part of the thesis a solution for ICI Mitigation in OFDM systems
is proposed. Simulation results show the effectiveness of the proposed solution,
and the simplified version of it, in different scenarios. Moreover, it is shown that
the algorithm converges after few iterations.
In the second part of the thesis, the application of OFDM to cognitive ra-
dio networks is presented. Specifically, after a brief overview of cognitive ra-
dio networks, it is shown how to exploit the specific featuresof OFDM signals
to recognize heterogeneous primary users in order to improve cognitive radio
adaptability. Performances are derived by evaluating the behavior of the CAF
Detector/Classifier and of the CR throughput adapter. Simulation results show
how CR throughput depends on each feature of heterogeneous PUsignals. More-
over, a Cognitive Radio Resource Management is developed by exploiting het-
erogeneous primary users and an OFDMA based resource allocation. The perfor-
mance of the proposed algorithm is evaluated in terms of total data rate achieved
by the CR users, and the satisfaction of CR users.
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Appendix
APPENDIX A. LIST OF ACRONYMS 127
Appendix A
List of Acronyms
3GPP 3rd Generation Partnership Project
A/D analog-to-digital
API Application Programming Interface
AWGN Additive White Gaussian Noise
BER Bit Error Rate
BS Base Station
BTS Base station Transceiver System
CA complex addition
CAF Cyclostationary Autocorrelation Function
CCC common control channel
CD complex division
CE Consumer Electronics
CIR Channel Impulse Response
CM complex multiplication
C-MAC Cognitive MAC
ComSoc Communications Society
CP Cyclic Prefix
CR Cognitive Radio
CRAHN Cognitive radio ad hoc network
CRM Cognitive Resource Manager
CRRM Cognitive Radio Resource Management
DAB Digital Audio Broadcasting
DARPA Defense Advanced Research Projects Agency
APPENDIX A. LIST OF ACRONYMS 128
DFT Discrete Fourier Transform
DVB Digital Video Broadcasting
EM Expectation-Maximization
EMC Electromagnetic Compatibility
ETSI European Telecommunications Standards Institute
FCC Federal Communications Commission
FDM Frequency Division Multiplexing
FFT Fast Fourier Transform
GI guard interval
HTTP Hypertext Transfer Protocol
ICI Inter-Carrier Interference
ICT Information and Communication Technology
IDFT Inverse Discrete Fourier Transform
IEEE Institute of Electrical and Electronic Engineers
IFFT Inverse Fast Fourier Transform
iid independent and identically distributed
ISI Inter-Symbol Interference
ISM industrial scientific and medical
LAN Local Area Network
LNA Low Noise Amplifier
LO local oscillator
LOS Line-Of-Sight
LTE Long Term Evolution
LTV Linear Time-Variant
MAC Medium Access Control
MAN Metropolitan Area Network
MMSE minimum mean square error
ML maximum likelihood
MPEG Moving Picture Experts Group
NO-VRR No-variable data rate requirements
NP-hard non-deterministic polynomial-time hard
OFDM Orthogonal Frequency Division Multiplexing
OFDMA Orthogonal Frequency Division Multiplexing Access
OS-MAC Opportunistic Spectrum MAC
PDP Power Delay Profile
PHY PHYsical layer
APPENDIX A. LIST OF ACRONYMS 129
POMDP Partially Observable Markov Decision Process
PU Primary User
QOS Quality Of Service
QPSK Quaternary Phase-Shift Keying
RF Radio Frequency
rms root-mean squared
RRM Radio Resource Management
RRS Reconfigurable Radio Systems
RTT round trip time
SC Single Carrier
SDR Software Defined Radio
SNR Signal to Noise Ratio
SYN-MAC Synchronized MAC
TAG Technical Advisory Group
TC Technical Committee
TCP Transmission Control Protocol
TGaf Task Group af
UDP User Datagram Protocol
UHF Ultra High Frequency
VRR variable data rate requirements
WiFi Wireless Fidelity
WiMax Worldwide Interoperability for Microwave Access
WRAN Wireless Regional Area Network
WSSUS Wide-Sense Stationary Uncorrelated Scattering
xG NeXt Generation