adam:arealisticimplementationforaw-cdma smartantenna...adam: a realistic implementation for a w-cdma...

EURASIP Journal on Applied Signal Processing 2004:9, 1384–1406c© 2004 Hindawi Publishing Corporation

ADAM: A Realistic Implementation for aW-CDMASmart Antenna

RamónMartı́nez Rodrı́guez-OsorioDepartment of Signals, Systems and Radiocommunications, Polytechnic University of Madrid, 28040 Madrid, SpainEmail: [email protected]

Laura Garcı́a Garcı́aDepartment of Signals, Systems and Radiocommunications, Polytechnic University of Madrid, 28040 Madrid, SpainEmail: [email protected]

Alberto Martı́nez OlleroDepartment of Signals, Systems and Radiocommunications, Polytechnic University of Madrid, 28040 Madrid, SpainEmail: [email protected]

Francisco Javier Garcı́a-Madrid VelázquezDepartment of Signals, Systems and Radiocommunications, Polytechnic University of Madrid, 28040 Madrid, SpainEmail: [email protected]

Leandro de Haro ArietDepartment of Signals, Systems and Radiocommunications, Polytechnic University of Madrid, 28040 Madrid, SpainEmail: [email protected]

Miguel Calvo RamónDepartment of Signals, Systems and Radiocommunications, Polytechnic University of Madrid, 28040 Madrid, SpainEmail: [email protected]

Received 30 May 2003; Revised 28 November 2003

Adaptive-type smart antennas do not usually operate on the deployed universal mobile telecommunication system (UMTS) sce-narios, although UTRA (UMTS terrestrial radio access) foresees their operation and they would improve capacity especially inmixed-service environments. This paper describes the implementation of a software radio-based version of an adaptive antenna,named ADAM, that can be used with any standard Node B, both in the up- and downlinks. This transparent operational featurehas been made possible by the partial cancelation algorithm applied in the uplink by means of a common beamforming vector.Firstly, a general description of the system as well as the theory of its operation are described. Next, the hardware architecture ispresented, showing the real implementation. Also a complete software description is done. Finally, results are presented, obtainedfrom both simulation and real implementation, showing the improvement obtained with the adaptive antenna as compared witha typical sectored one. Performance results obtained in the initial tests show that ADAM prototype provides an SINR increase of12.5 and 6.5 dB over a conventional sectored antenna in the uplink and downlink, respectively. System-level simulation results arepresented, showing the throughput increase obtained with ADAM. These findings provide evidence of the capacity improvementachieved with the ADAM prototype.

Keywords and phrases: smart antenna prototype, beamforming, wireless communications, synchronization, DSP, UMTS.

1. INTRODUCTIONThe smart antenna concept is applied to several kinds of an-tenna arrays. Phased arrays, switched multibeam antennas,and adaptive array antennas are usually included under thesmart antenna concept with the only condition of includ-

ing the possibility to somehow control the radiation pattern.Great advantages have been reported for the smart antennaimplementation in base stations for mobile telephone com-munications, but this kind of antenna has not been exten-sively applied to those systems yet.

mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]

ADAM: A Realistic Implementation for a W-CDMA Smart Antenna 1385

If capabilities of phased array, switched-beam array, andadaptive array antennas are compared, the last type showsconsiderable advantages over the others [1]. Not only canadaptive arrays improve antenna gain in the user directionbut they can also cancel interferences inside the angularrange of control. This ability implies an increase of the signal-to-interference-plus-noise ratio (SINR) for each user. Forcode division multiple access (CDMA) systems, an increaseof sector capacity is obtained for those cells with base sta-tions equipped with smart antennas. The capacity increase ishigher in cells with high interference levels, usually producedby high bit rate users.

Adaptive antenna systems can be implemented using aspace or time reference-based algorithm. In spatial referenceadaptive arrays, interference directions are computed and thearray weights are obtained to cancel or minimize them. Intime reference adaptive arrays, time series from the input sig-nal at each array element are processed to form the array vec-tor of weights. The array factor implemented for each userincreases the SINR and improves the energy per bit to noisedensity ratio (Eb/N0) due to the correlation of the receivedsignals. This strategy is appropriate for CDMA signals since atime reference can be obtained applying the user code. In theparticular case of universal mobile telecommunication sys-tem (UMTS), the physical layer has been designed to workwith adaptive antennas both in uplink and downlink [2].

A significant research effort has taken place in the lastyears to introduce smart antenna systems in cellular sce-narios. However, the deployment of these antenna systemshas not become a reality yet due to their cost and com-plexity. In practice, only switched-beam antennas for secondgeneration (2G) systems have been commercially deployed[3, 4, 5, 6, 7, 8]. This is due to the complexity of adaptiveantennas in third generation (3G) systems. In contrast to 2Gsystems, where beamforming can be done in radio frequency(RF), beamforming in 3G must be applied after demodu-lating the CDMA signal so that adaptive antenna functionsneed to be integrated into the (digital and intermediate fre-quency (IF)) baseband-processing sections of the base sta-tion. Therefore, the implementation of adaptive antennas in3G base stations requires a reconfigurable and flexible archi-tecture. These features can be obtained using software radioplatforms [9, 10, 11].

Many of the existing smart antenna solutions for 3G havebeen developed for a unique base station equipment manu-facturer [12, 13]. This fact makes the deployment of smartantenna systems unfeasible for mobile communications op-erators due to the high associated cost and manufacturer de-pendency. A plug and play smart antenna solution, appropri-ate for any base station from any manufacturer, has not beendeveloped yet.

This paper details a practical implementation of an adap-tive plug and play smart antenna for 3G mobile communi-cation systems based on wideband-CDMA (W-CDMA) likeUMTS [14, 15]. Unlike currently existing adaptive antennaarrays, the implementation described here implies an easydeployment over any base station, not only on those specifi-cally developed to be used with smart antennas [16]. ADAM

stands for “adaptive antenna for multioperator scenarios,” asit can be connected to any base station site even shared byseveral operators.

As a plug and play functionality is demanded, the UMTSsignals are demodulated and remodulated again, allowing adirect connection between the smart antenna outputs andthe base station inputs [16]. Due to this process, in the up-link, only those interferences common to the intracellularusers and all the extracellular interferences are canceled. Therelationship between the extracellular and intracellular inter-ferences is called the extracellular interference factor F andhas a value between 0.4 to 1.4 depending on the environmentand the service [15]. This implies that more than 50% of theinterferences are canceled on average as the common intra-cellular interferences should also be taken into account.

This antenna will take profit of hot spots, improving thecapacity in the vicinity of high occupied cells. In these situa-tions, mainly higher power external interferences from mul-timedia services are canceled by ADAM prototype, as it isdemonstrated by simulation in this paper. In these situations,the antenna would help the cells in the vicinity of a hot spotto expand their coverage and to compensate the “cell breath-ing” of high occupied cells. Moreover, in mixed and asym-metric services scenarios, typical of 3G systems, ADAM willincrease the capacity in terms of total throughput.

According to the software radio concept, the analog-to-digital conventers (ADCs) and digital-to-analog conventers(DACs) are located just before the analog RF-to-IF chains,hence working with IF signals instead of the typical base-band signal. This allows most of the system modules to beimplemented in software, which is a great advantage with re-spect to pure hardware implementations because the systemcan be easily reconfigured and updated with more advancedversions. Therefore, a great flexibility is achieved with thisstructure.

The beamforming module has been implemented justbefore the W-CDMA modulation. In the uplink, classicalbeamforming algorithms have been adapted to the specialextracellular cancelation scheme implemented [17, 18]. Al-though different beamforming algorithms can be used, thenormalized least mean squares (NLMS) algorithm has beenselected initially due to its reduced computational complex-ity. In the downlink, beamforming aims to cancel all intra-and extracellular interferences, thus a full cancelation algo-rithm has been selected.

Apart from NLMS, some tests have been done using therecursive least squares (RLS) algorithm in order to studythe performance improvement obtained in the convergencespeed and final SINR.

It is important to remark on the implementation of thesynchronization algorithms in UMTS [19, 20, 21]. This prob-lem has been solved using a two-step approach, initially do-ing a coarse synchronization that is followed by a continuousfine synchronization. The implemented algorithm has beenintensively optimised.

As the smart antenna should be transparent for the basestation, it should not implement the base stations physicalprocedures, such as power control and handover, which are

1386 EURASIP Journal on Applied Signal Processing

Mobile

DPCHuplink

Downlink

PDSCH

Smartantenna Beamformer

Antennaarray

RF-IF

conversion

Weights[w]

Modem

IF-RFconversion DPCH

uplink

Downlink

PDSCH-CPICH

Node B

Figure 1: Implementation architecture of the ADAM smart antenna to be deployed in connection with a standard Node B.

performed by the base station (Node B) itself. Moreover, po-larization diversity is performed by the base station, and theADAM antenna is connected to both base station ports andprocesses each polarization independently.

2. UMTS SMART ANTENNA ARCHITECTUREANDOPERATIONOF ADAM PROTOTYPE

The implemented architecture of the ADAM smart antennaprototype is shown in Figure 1. In the downlink, the RF sig-nal from Node B is downconverted to IF, digitized, demod-ulated, beamformed (with a set of different weights for eachuser), and finally, upconverted to RF. In the uplink, an equiv-alent process is performed but using a common beamform-ing vector for all the users. This architecture performs a totalinterference cancelation in the downlink but only a partialcancelation in the uplink.

However, a higher flexibility is achieved because ADAMantenna can be plugged to any base station, even those not es-pecially designed to work with a smart antenna system [16].All the commercial Nodes B have a standardized RF interface(Uu interface). In case of using a baseband interface for theconnection of the smart antenna with the base station, theinterface definition would depend on each particular man-ufacturer, and ADAM prototype would lose its transparentoperation feature. Therefore, once the array output has beencomputed, it must be upconverted again to the original RFcarrier in order to interface adequately with any standardNode B, as it can be seen in Figure 1.

According to the physical layer of UMTS, time refer-ence and user synchronization may be obtained in the uplinkfrom the dedicated channel (DCH) (in particular, dedicatedphysical control channel (DPCCH)) [17]. However, down-link allows several ways to obtain time reference and usersynchronization: common pilot channel (CPICH), primarycommon control physical channel (P-CCPCH), secondary-CCPCH (S-CCPCH), and even pilot symbols or diversity pi-lots [15]. ADAM implementation gets user synchronizationfrom DPCCH in the uplink, and from CPICH in the down-link. Tables 1 and 2 summarize which physical channels areprocessed in up and down streams to get system informationand which channels are beamformed or not by the ADAMprototype.

In the uplink, both common and dedicated channels arebeamformed since the beamformer for dedicated channels

Table 1: Beamforming of uplink physical channels. (PRACH: phys-ical random access channel.)

Channel Function in smart antenna Beamforming

DPCCHUser synchronization and uplink

channel characterizationYes

DPDCH —– YesPRACH —– Yes

Table 2: Beamforming of downlink physical channels. (AICH: ac-quisition indicator channel; CSICH: common packet channel statusindicator channel; PICH: page indication channel; PDSCH: physi-cal downlink shared channel.)

Channel Function in smart antenna Beamforming

SCH Cell slot synchronization NoCPICH Downlink frame synchronization No

User synchronization(scrambling code identification)

P-CCPCH —– NoS-CCPCH —– NoAICH —– NoCSICH —– NoPICH —– NoDPCH —– YesPDSCH (DPCH) —– Yes

adapts simultaneously the common channels coming fromthe users directions. Figure 2 shows the proposed architec-ture for the uplink, where the DPCCH from each user is syn-chronized and demodulated to perform the computation ofindividual beamforming weights. At this stage, the commonset of weights are computed and applied to the composite re-ceived UMTS signal.

In the downlink, common and broadcast channels arebypassed and transmitted to the whole sector in parallel withthe beamformed dedicated channels, as it can be seen inFigure 3. The synchronization is performed using primary-CPICH (P-CPICH) information and applied to every user tobe demodulated, beamformed, and remodulated again be-fore being sent to each antenna element. Downlink weightsare obtained from uplink weights, as it will be explained inSection 4.2.

The performance improvement that may be achievedwith an adaptive antenna depends on the following aspects:


Dedicated channels&

common channels

RF modules

RF/IF

RF/IF

RF/IF

RF/IF

A/D

A/D

A/D

A/D

Antennaarray

Modem & beamforming modules(for each user)

Demodulation(only DPCCH)

Weights-computation

module

Synchronizationmodule

Uplink weightscombination

module

Combination of signalsfrom the array(beamforming)

D/A IF/RF

StandardizedRF interfacefor Node B

(Uu)

Node B

Figure 2: Uplink block diagram (1 polarization).

RF modules

RF/IF

RF/IF

RF/IF

RF/IF

D/A

D/A

D/A

D/A

Antennaarray

Delay bufferCommon channels

Modem & beamforming modules(for each user)

Remodulation DemodulationSynchronization

stage

Dedicated channels

Downlinkweightsgenerator

Uplinkweights

(for each user)

A/D RF/IF

StandarizedRF interfacefor Node B

(Uu)

Node B

Figure 3: Downlink block diagram (1 polarization).

antenna array geometry, adaptive algorithm that controls thebeamforming process, and propagation and interference en-vironment. Those issues have been studied by simulation andare presented in Section 6. The ADAM array prototype usesfour commercial sectored antennas for the UMTS band, eachwith a −3 dB beamwidth of 65◦ and ±45◦ polarization ports[22]. The individual antennas are put together in a uniformlinear array structure, as shown in Figure 4. With this config-uration, interelement separation is 15 cm (wide dimensionof each sectored antenna), which is equivalent to 0.975λ and1.070λ at the uplink and downlink frequencies, respectively.

3. HARDWARE ARCHITECTURE

The overall system proposed in this paper is formed by sev-eral hardware devices. Their characteristics, as well as the fi-nal selected hardware architecture, are presented below forboth the uplink and downlink. Although a general descrip-tion of the adaptive antenna has been made in Section 2, wefocus here on the specific selected hardware solutions.

In the uplink, the received analog signal is downcon-verted by the RF-to-IF chains and digitalized. Afterwards, itis processed in the digital signal processing module, where


Figure 4: ADAM prototype: antenna array structure.

several digital signal processors (DSPs) work in parallel. Theprocessed baseband signal is then analog-converted again,sent to an IF-to-RF chain and then to the Node-B RF inputport. Conversely, the signal received from the Node-B out-put port follows similar steps in the downlink (RF-to-IF con-version, digitalization, digital processing, analog conversion,and RF upconversion), being finally transmitted through theantenna array.

Figure 5 shows a general architecture of the hardware im-plementation, where the blocks for the two polarizations areidentical. The digital processing module, formed by severalprocessors, is common to both polarizations. Analog RF-to-IF and IF-to-RF chains are not thoroughly explained heresince it is out of the scope of this paper, mainly focused on thedigital signal processing stages. Figure 6a shows the develop-ment system for software radio modules, whereas Figure 6bshows the test equipment.

Due to the software radio implementation, the IF fre-quency value offered to the rest of the modules must be care-fully selected. A high IF would simplify the design of theanalog chains, especially the filtering of the image frequency,but it would increment the processing capacity requirements.Also the current state of the art in ADCs and DACs should betaken into account since there is a tradeoff between the ver-tical resolution and sample frequency that can be achieved.With this in mind, an IF of 44MHz was selected as a com-promise solution.

Several aspects were taken into consideration to prop-erly select the ADCs and DACs. The first one was the ver-tical resolution (or number of bits in conversion) requiredfor this application. The quantification noise is lower witha high vertical resolution, but the available maximum sam-pling frequency decreases as the number of bits in conver-

sion are incremented. The recommended number of bits touse in a UMTS application is at least 12 [1]. As for the maxi-mum sampling frequency fs,max, it should be high enough tocorrectly receive or transmit the desired signal without lossof information. Also related to the fs,max, we have to takeinto account the conversion bandwidth parameter. Finally,the dynamic range of the input voltage should be considered,especially in the analog chains design, to properly adjust itsgain to the ADC input and DAC output levels.

After the ADC, the signal must be downconverted tobaseband by means of an IQ demodulator. One possibil-ity could be to implement it directly in a general DSP. Butdue to the high UMTS sampling rate, the required compu-tational capacity to accomplish that operation would makethe implementation unfeasible. Another interesting solutionwould be to use on-chip IQ demodulators or broadbanddownconverters, usually called front-ends. These devices canprocess the signal independently of the general DSPs, whichcan be used then to do the subsequent processing. The lat-ter option has been chosen to implement the downconver-sion to baseband; so a general-purpose receiver has been se-lected from the commercially available devices. The selectedreceiver boards1 consist of two broadband IQ demodula-tors plus two ADCs so that two identical receiver channelsper receiver board are available [23]. The vertical resolutionfor the ADCs is 12 bits, and its maximum sampling fre-quency is 80MHz. The ADC sampling frequency must becarefully selected. It has to be a multiple of the UMTS base-band signal rate 3.84Mchip/s, multiplied by the number ofsamples per chip, which is Nspc = 4 in this prototype. Nei-ther 15.36MHz nor 30.72MHz can be used as sampling fre-quencies since it would cause aliasing in the sampled sig-nal. On the other side, the ADC features restrict the possi-ble sampling frequency to a maximum of 100MHz. Thus,fs = 61.44MHz has been chosen. Since fs does not meet theNyquist theorem ( fs is lower than 2 · IF), the resulting sig-nal is undersampled. This does not involve a loss of infor-mation because the signal is bandlimited to 5MHz. A dia-gram of the main parts of one receiving channel is shown inFigure 7.

Similarly, an IQ modulator is required before each DAC.Also the front-end solution has been adopted here. The se-lected digital upconversion boards2 provide two identicaland independent broadband channels [23]. The DAC accepts12-bit digital signal as input, and its maximum sampling fre-quency is 200MHz. A block diagram of one channel can beseen in Figure 8.

Once the signal has been digitally converted and IQ de-modulated, it has to be processed by the synchronization andbeamforming modules, which are implemented in general-purpose digital processors. A few characteristics have beenconsidered to select the DSPs that have been used to imple-ment the software modules. The most important features arethe arithmetic type, the clock rate and, in connection with

1Pentek 6235-board.2Pentek 6229-board.


Polarization 2

Polarization 1

Duplexor

Duplexor

Duplexor

Duplexor

RF-to-IF chain 1

RF-to-IF chain 2

RF-to-IF chain 3

RF-to-IF chain 4

IF-to-RF chain 1

IF-to-RF chain 2

IF-to-RF chain 3

IF-to-RF chain 4

AD

IQreceiver

AD

IQreceiver

AD

IQreceiver

AD

IQreceiver

DA

IQupconverter

DA

IQupconverter

DA

IQupconverter

DA

IQupconverter

Digital processing

Quad

DSP

DSP

DSP

DSP

Quad

DSP

DSP

DSP

DSP

Quad

DSP

DSP

DSP

DSP

Racewayinterlink

Quad

DSP

DSP

DSP

DSP

Quad

DSP

DSP

DSP

DSP

Quad

DSP

DSP

DSP

DSP

Polarization 2Polarization 1

DA

IQupconverter IF-to-RF chain 5

AD

IQreceiver

RF-to-IF chain 5

Basestation(Node B)

Monitor PC

Figure 5: General hardware structure.

(a) (b)

Figure 6: Hardware modules of ADAM prototype and test equipment. (a) Development system. (b) Measurement and test system.

this, the computational capacity. Fixed-point arithmetic ispreferred instead of floating-point arithmetic since a higherspeed processing for linear operations, like the ones requiredin this application, can be achieved. As regards the clock rate,the higher it is, the greater the number of instructions persecond that can be executed, and the higher the computa-tional capacity that can be obtained. In order to increase thecomputational capacity, a structure of various DSPs in paral-

lel can be used. The selected digital processing structure con-sists of six 4-DSP boards,3 referred to as Quads [23]. EachQuad is formed by four 300-MHz fixed-point DSPs alongwith other interfaces between DSPs. Every Quad is capable of

3Pentek 4292-Quad VME board, with four Texas instrument

TMS30C6203 processors.


A/D converter & digital downconverter

From RF/IFmodule

A

D

Fs

90◦

Digital sinegenerator IF

Digital mixer(IQ demodulator)

Decimation filter(1/Nspc)

Decimator digitalfilter

I branch

To DSP

Q branch

Figure 7: ADC and IQ demodulator.

D/A converter & digital upconverter

To IF/RFmodule

Bandpassfilter

D

A

Fs

12

90◦

Digital sinegenerator IF

Digital mixer(IQ modulator)

Interpolation filter

Interpolationdigital filter

12

12

I branch

From DSP

Q branch

Figure 8: DAC and IQ modulator.

delivering a combined peak processing power of 9600 MIPS(millions of instructions per second).

In order to increase the data transfer rate between Quads,a high-speed data bus has been used4 [23, 24]. This de-vice is a high-speed backplane fabric capable of deliver-ing 32-bit word transfers between versa module eurocard(VME) boards, such as the Quads presented previously. Itprovides multiple, simultaneous high-speed communicationpaths between DSPs which make the bus a valuable asset toreal-time applications. The bus is capable of communicatingup to eight VME boards at a data transfer rate of 267MBps,which means an aggregate transfer rate up to 1068GBps.

For monitoring tasks, a personal computer can be con-nected to the digital processingmodule to control the processand allow viewing of key variables and parameters.

4. PRINCIPLES AND IMPLEMENTATIONOF SOFTWARE RADIOMODULES

The software implementation has been divided into twomain submodules: the set-up, synchronization and modemmodule, and the adaptive beamforming module. They arethoroughly explained below.

4Pentek 8251 Race++ interlink modules.

4.1. Set-up, synchronization, andMODEM stages

As it is known [2], each physical channel in W-CDMAis spread combining two types of codes with complemen-tary properties: orthogonal variable spreading factor (OVSF)channelization codes and scrambling codes (Gold codes,with excellent correlation properties). Basic informationneeded in a W-CDMA process is the used codes and, like anyspread-spectrum technique, the timing reference [25]. Thefunction of the set-up stage is to find the essential data neededbefore the demodulation process in uplink and downlink.

4.1.1. Set-up procedure

Basic synchronization algorithms employed in the modemwill be detailed in Section 4.1.2, and they are common foruplink and downlink. The main difference between uplinkand downlink synchronization stages lies in which physicalchannels are used as reference signals.

In the downlink, all the physical channels (common sig-nalling channels and dedicated user channels) use the samesynchronization reference, that is, if the synchronization ofone channel is known, the timing of the other channels is au-tomatically known. The procedure to find the common tim-ing reference for all downlink channels is called cell searchprocedure. Typically, cell search procedure is completed af-ter three steps: slot synchronization, frame synchronization,


Table 3: Number of clock cycles and acquisition time for the coarse synchronization algorithm.

Branches Clock cycles/bit Acquisition time (number of frames)

Serial search 1 3000 1

Parallel search

2 5700 0.5

3 8700 0.33

4 10800 0.25

10 27300 0.1

code-group identification, and finally scrambling code iden-tification [2]. Common signalling channels needed in thisstage are the synchronization channel (SCH) and the P-CPICH.

The first and second steps use SCH codes. During thefirst step, the cell slot synchronization is acquired; it can bedone by correlating the received signal of the base stationwith the primary SCH codes, employing the coarse synchro-nization algorithm, as it will be explained in Section 4.1.2.1.After the cell slot timing is achieved, the frame synchro-nization procedure is initiated. In this second step, the sec-ondary SCH codes must be used. Once the combination ofsecondary SCH codes used by the base station is identified,it is possible to acquire the general frame synchronizationfor downlink and the primary code group of cell simultane-ously.

Finally, the exact primary scrambling code used by thecell is determined in the third step. This search is limited tothe set of eight different scrambling codes determined by theprimary code group. The reference channel employed in thisstep is the P-CPICH, which is transmitted continuously overthe entire cell. The P-CPICH is an unmodulated code chan-nel, which is scrambled with the cell-specific primary scram-bling code of the cell. The P-CPICH is unique for each cell.After the primary synchronization code has been identified,the cell search procedure is finished and it is possible to ap-ply the general fine synchronization algorithm in downlinkwith the P-CPICH channel. At the same time, the P-CCPCHis demodulated in order to extract the specific parametersnecessary for user’s demodulation, which are the channeliza-tion code, spreading factor, and the specific timing delay, forthe downlink, and the scrambling and channelization codes,spreading factor, and DPCCH format, for the uplink. Thecombination of the cell search procedure and extraction ofuser’s specific information is denoted as set-up stage of themodem.

Unlike downlink, each user has a specific synchroniza-tion reference in the uplink. If the modem knows the pa-rameters of active users for uplink (obtained in the downlinkset-up stage), the synchronization scheme is very simple. Foreach user, the timing reference is extracted from the DPCCH,applying the coarse and fine synchronization algorithms di-rectly.

4.1.2. Synchronization algorithms

The timing information of the transmitted frame is essentialin order to properly demodulate the despread signal. Even

if there is a single chip duration error, the received spreadspectrum signal cannot be properly demodulated.

Once the used codes in physical channels have been ob-tained, the appropriate timing reference is extracted. Thissynchronization issue is resolved following a two-step ap-proach [20]. Firstly, coarse synchronization or initial codeacquisition accomplishes the synchronization of the receivedsignal and the corresponding code, with an uncertainty ofhalf a chip period (±Tc/2). Secondly, fine synchronization orcode tracking performs and maintains the synchronizationbetween the received signal and the code with a precision al-ways lower than half a chip period.

To perform the synchronization, the scrambling codeproperties are used. These codes have an autocorrelationfunction that reaches its maximum when the code and thereceived signal are aligned.

4.1.2.1. Coarse synchronization

As stated before, the objective of the coarse code synchro-nization is to achieve an initial code acquisition between thereceived signal and the corresponding scrambling code. Thisis equivalent to matching the phase of the spreading signalwith the code.

There are different general acquisition techniques [19, 20,21]. In the serial search, all the possible phases are tested oneby one sequentially. The complexity for this method is quitelow but the associated acquisition time is high. In the parallelsearch, all the possible phases are tested simultaneously. Thecomplexity is higher but the acquisition time is much lowerthan in the serial search. An intermediate approach betweenthe serial and parallel search strategies has been implementedin order to achieve the coarse synchronization with a mod-erate computational load, considering the complexity versusacquisition trade-off. A study of the computational load re-quired by the different implementation approaches is shownin Table 3.

Considering the capacity of the used DSP’s, the three-branches serial-parallel approach has been implemented.The block diagram of the coarse synchronization stage isshown in Figure 9.

In the figure, several blocks can be distinguished: corre-lators, thresholds generator, signal control modules, and ascrambling code generator. The received match-filtered sig-nal is correlated with different cycle-delayed code versions.The maximum correlation value from the branches is com-pared with the first threshold γ1 which is obtained taking intoaccount the second maximum correlation value. In order to


From matchedfilter

· · ·

M parallelbranches

Correlation

∣∣∣∣ 1Ncod∑

Ncod

∣∣∣∣2

a(n)

Branch 1

Correlation

∣∣∣∣ 1Ncod∑

Ncod

∣∣∣∣2

a(n− 1/M ·Ncod)

Branch 2

Turned code1/M ·Ncod

Correlation

∣∣∣∣ 1Ncod∑

Ncod

∣∣∣∣2

a(n− 1/M ·Ncod) BranchMTurned code

(M − 1)/M ·Ncod

PN codegenerator

a(n)

To coarsesynchronization

Thresholdsgenerator

Maxcorr

γ1

No

Yes

AND

Avgcorr

γ2

Yes

No

To coarsesynchronization

To finesynchronization

Figure 9: Block diagram of coarse synchronization.

From coarsesynchronization

Decimator

On-time sample

DPCCH demodulator

a(n)

1Ncod

∑Ncod | · |2

Demodulated bits

Early sampleCorrelation

+Tc

a(n)

1Ncod

∑Ncod

| · |2 Max

Late sampleCorrelation

−Tca(n)

1Ncod

∑Ncod

| · |2

Codegenerator

Figure 10: Block diagram of fine synchronization.

avoid situations in which the background noise may cause awrong correlation which exceeds the first threshold, it is nec-essary to set another threshold to minimize this effect. Thissecond threshold γ2 is calculated from the average of all thecorrelations except the maximum value. If the input signalsurpasses both thresholds, then it is coarse-synchronized andfine synchronization is triggered.

4.1.2.2. Fine synchronizationThe purpose of code tracking is to perform and maintain thesynchronization. Code tracking starts its operation only aftercoarse synchronization has been achieved. After coarse syn-chronization, a small phase error is still present. In order tocorrect this error, the loop structure shown in Figure 10 isused [19].


Antenna 4

. ..

Antenna 1

From RF(antenna 4)

. ..

From RF(antenna 1)

DescramblingUser 1,N DPCCH

Despreading

Bits DPCCH user 1,N

...

DescramblingUser 1, i

DPCCHDespreading

Bits DPCCH user 1, i

...

DescramblingUser 1, 1 DPCCH

Despreading

Bits DPCCH user 1, 1

SDPCCH,n

Scramblinguser code

cn

Spreadinguser codes

Bits DPCCH user 4,N

Bits DPCCH user 4, i

Bits DPCCH user 4, 1

To beamformingmodule

Figure 11: Uplink demodulator diagram.

The first block is a decimator that selects the correct sam-ple at the right time, depending on the correlation value.In the second step, the decimated signal is delayed or ad-vanced half a chip period, creating the late, early, and on-timebranches. These three signals are correlated with the locallygenerated scrambling code, and themaximum absolute valueof the correlations is selected. According to this selection, thetiming information is updated.

4.1.3. Demodulation in uplink and downlink

Once the timing information and scrambling and channel-ization codes are determined, any UMTS physical channelcan be demodulated.

In the uplink, DPCCH is demodulated for each user inorder to extract the pilot bits that will be used as the referencesignal in the beamforming process. To complete this task, twooperations must be carried out: the complex-valued signal isdescrambled by a complex-valued scrambling code SDPCCH,nwhich identifies a user, and the signal is despread using thechannelization code cn which identifies the DPCCH channel.This process is shown in Figure 11.

In the downlink, the dedicated physical channel (DPCH)is demodulated. Firstly, the signal from Node B is de-scrambled by a complex-value scrambling code Sdl,n whichidentifies the cell and afterwards, the signal is despreadthrough the correlation with a real-valued channelizationcode cch,SF,n which identifies the user in the downlink. Bothtime-multiplexed DPCCH and DPDCH (dedicated physicaldata channel) bits are obtained after this operation. Once theDPCH bits for every user have been demodulated and beam-formed, the spreading operation is performed with cch,SF,nand scrambled with Sdl,n. The block diagrams of the modemfor the downlink are shown in Figures 12a and 12b.

4.2. Adaptive beamformer

Immediately after the synchronization has been achieved, thefollowing stage is the adaptive beamforming. The aim of thismodule is to calculate the set of array weights that make thearray output signal satisfy an optimization criterion. Apartfrom this computation, the beamformingmodule adequatelycombines the received signal vector in order to produce aspatially filtered W-CDMA signal in the array output.

In the downlink, the base station transmits a separatebeam pointing at the direction of each user, along with thebroadcast channels, which are transmitted to the whole sec-tor.

In this section, beamforming principles and implemen-tation aspects are thoroughly explained. Moreover, theoret-ical expressions for the SINR are given for the operation ofADAM in uplink and downlink. In CDMA systems, this pa-rameter is used for the estimation of capacity, throughput,and quality of service. Performance results will be shown inSection 6.1.

4.2.1. Uplink operation and implementation

Let x(t) be the complex envelope representation for the vec-tor of received signals in the array elements. For a situationwith K mobile users and one interfering source i(t), the vec-tor x(t) can be expressed as follows:

x(t) =K∑k=1

√Pk

Lk∑l=1

αUkl(t)aU(θkl)sk(t − τkl

)

+√Pinta

U(θint)i(t) + n(t),

(1)

where Pk is the power transmitted from user k, αkl and τklare the complex channel gains and delay of the l-path of the


From RF

S∗dlCell scrambling

code

Despreading

Despreading

Despreading

Bits DPCH user N

...

Bits DPCH user i

...

Bits DPCH user 1

To beamformingmodule

cch,SF,nSpreading user codes

(a)

Antenna 4...

Antenna 1W1,NH∗DPCHbitsuser n

W1,iH∗DPCHbitsuser i

W1,1H∗DPCHbitsuser 1

...

...

Spreading

Spreading

Spreading

cch,SF,n

Spreading user codes

Sdl,n

Cell scramblingcode

To RF module(antenna 4)...

To RF module(antenna 1)

(b)

Figure 12: Downlink demodulator and modulator diagrams. (a) Downlink demodulator. (b) Downlink modulator.

kth user, and a(θ) = g(θ) · {exp( j(2π/λ)d(l− 1) cos(θ)), l =1, . . . ,L} is the response of a uniform linear array with L an-tenna elements and an interelement separation d, such asADAM, to a wave impinging from an azimuth direction θ,including the element antenna pattern g(θ) [26]. The kthuser signal sk(t) includes modulation, data, and spreading.Pint is the power transmitted from the external interferencesource. SuperscriptU stands for the uplink. Finally, n(t) is anL-dimensional complex Gaussian vector with independentand identically distributed (i.i.d.) components of zero meanand variance given by the corresponding signal-to-noise ra-tio (SNR).

In the uplink operation, two alternatives can be consid-ered. The first one consists in performing a total cancelationof interfering sources for each user, including the contribu-tions from other mobile users. Let wk be the uplink beam-forming vector for each particular user in the total cancela-tion scheme. With this approach, if K users are present inthe cell, then a separate beamformed signal yk(t) = wHk x(t),k = 1, . . . ,K , should be transferred to Node B. Therefore, Kseparate input channels would have to interface with Node B,and ADAM operation would lose its transparent behavior.

The other alternative is to apply a common beamformingweight vectorw to the composite received signal x(t) (mobileuser signals plus interference sources). The approach appliedto ADAM is to use a linear combination of wk weights to per-form the common beamforming operation that is required inthe uplink. All individual beamforming vectors have a com-mon feature, namely, the cancelation of interfering sourcesexternal to the system. Following this technique, the arrayoutput can be expressed as follows:

y(t) =K∑k=1

wHk x(t) =

K∑k=1

wHk

x(t) = wHx(t)

= wH

K∑k=1

√Pk

Lk∑l=1

αUkl(t)aU(θkl)sk(t − τkl

)+√Pintw

HaU(θint)i(t) +wHn(t).

(2)

This scheme is called partial interference cancelation be-cause only common interfering sources will be canceled afterapplying common beamforming weights. The uplink SINRin the array output for user k is therefore given by

SINRULk

=wHE

{∣∣∣√Pk∑Lkl=1 αUkl(t)aU(θkl)sk(t − τkl)∣∣∣2}w

wHE

{∣∣∣∣∑K−1i=1i �=k

√Pi∑Li

l=1 αUil (t)a

U(θil)si(t − τil

)∣∣∣∣2}w

+wHE{∣∣√PintaU(θint)i(t)∣∣2}w +wHE{∣∣n(t)∣∣2}w

.

(3)

The second term in the denominator represents the com-mon interference contribution that appears in the array out-put. The level of common interference cancelation is givenby the magnitude of |wHaU(θint)|2.

In both alternatives, the calculation of individual beam-forming weights wk fulfils the minimum mean square error(MMSE) criterion in the array output. The optimum solu-tion is given by the Wiener-Hopf equation as wk = R−1k pk,where Rk = E{xk(n)xHk (n)} and pk = E{xk(n)d∗k (n)}, xk(n)and dk(n) being the vector of demodulated pilot bits in theantenna array and the reference pilot bits, respectively. Thisequation does not represent a practical solution so that a sub-optimum set of weightsmust be calculated bymeans of adap-tive algorithms. This procedure is based on the iterative esti-mation ofwk each time a new pilot bit is demodulated. In thisway, the antenna is capable of adapting its radiation patternto a fast varying environment.

Two well-known adaptive algorithms have been consid-ered, namely, NLMS and RLS, whose update equations areshown in Figures 13a and 13b, respectively. The first one isthe NLMS, which is based on the instantaneous estimationof Rk and pk, and only vector operations must be performed.Due to its simplicity and reduced computational complexityof O(L), NLMS is very suitable to a practical implementationthat must comply with real-time requirements.


Initializationwk(0) = [1 0 · · · 0]H

0 < µ < 1

e(n) = dk(n)−wHk (n− 1)xk(n)

wk(n + 1) = wk(n− 1) +µ

‖xk(n)‖2xk(n)e

∗(n)

n = n + 1;

Array output yk(n) = wHk (n)xk(n)

(a)

Initialization

wk(0) = [1 0 · · · 0]H

Rk(0) = 1ε0 I , 0 < ε0 � 10 < δ0 < 1

ε(n) = dk(n)−wHk (n− 1)xk(n)

R−1k (n) = δ−10 R−1k (n− 1)−δ−20 R−1k (n− 1)xk(n)xHk (n)R−1k (n− 1)

1 + δ−10 xHk (n)R−1k (n− 1)xk(n)

wk(n + 1) = wk(n− 1) + ε∗(n)R−1k (n)xk(n) n = n + 1;

Array output yk(n) = wHk (n)xk(n)

(b)

Figure 13: Update weight equations. (a) NLMS algorithm. (b) RLS algorithm.

On the other hand, RLS is based on the iterative esti-mation of the autocorrelation matrix Rk, which imposes ahigher computational load than NLMS, although its con-vergence speed is faster. Matrix operations make RLS un-feasible for real-time implementations, being the complex-ity of O(L2). Further information on these algorithms can befound in [17, 18].

Regarding the implementation aspects, Figure 14 showsthe beamforming structure used in the uplink. DemodulatedDPCCH bits from the modem and the received signal vectorx(t) are the inputs of the beamforming module. These bitsare used to acquire the slot synchronization, which is neces-sary to obtain the correct pilot bits that have to be used asthe reference signal in the weight computation process. Afterthat, the single-user weights are computed, and the commonbeamforming weight vector is calculated. The output of thebeamforming process is obtained by multiplying the receivedsignal vector by the common weight vector and combiningthe resulting signal vector, without the need of demodulat-ing each user’s data channels. It must be noticed that the sig-nal vector impinging in the array antenna is composed ofcontributions from several users so that both the slot syn-chronization and the single-user weight computation mustbe executed in parallel for every serviced user.

4.2.2. Downlink operation and implementation

Optimal downlink beamforming will minimize the inter-ference received by other users and will enhance the usefulsignal power received by the desired user. Due to the fre-quency translation that appears in a frequency division du-plex (FDD) system, uplink and downlink communicationscenarios are different. As a consequence of the lack of down-link channel information, the calculation of transmissionweights is all but an easy task.

One of the methods for estimating downlink weightsfrom uplink channel information is the use of the uplink spa-tial covariance matrix [27]. However, this approach involvesthe use of many computational resources.

In order to reduce the complexity of the implementa-tion, ADAM uses individual uplink weights as transmissionvectors. Due to frequency translation (∆ f = 190MHz), thetransmitting array factor is similar but not the same as in theuplink. Therefore, the synthesized beam will not be point-ing exactly in the direction of the mobile user, and the inter-ference received by other user will be reduced but not mini-mized.

For downlink, after joint transmission of the weightedsignals bounded for the K users from the base station, the


From modem module

Uplink beamformermodule 1 Demodulated

control bits, user i

Slot synchrmoduleuser i

Single-user weightcomputation

moduleuser i

1

wi,1 · · ·wi,LL User iL User 1

Total weightscomputation module

L w1 · · ·wL

From Lantennas

Receivedsignals

Combiner

w1 w2 wL

· · ·∑

Processedsignal

1

To basestation

Figure 14: Beamformer structure: uplink.

baseband signal received by the mobile k, xk(t), is given by

xk(t) =K∑k=1

√Pkw

Hk

Lk∑l=1

αDkl(t)aD(θkl)sk(t − τkl

)+ nk(t), (4)

where wk is the transmission beamforming vector for user k,Pk is the power assigned to the user k signal, and nk(t) is acomplex white Gaussian process that represents the thermalnoise contribution in the mobile user equipment. The otherelements of (4) have the same meaning as in (1). In an FDDsystem, uplink (αUkl) and downlink (α

Dkl) fading coefficients

are uncorrelated, and in the simulations, they have been gen-erated from independent Rayleigh fading processes.

The SINR perceived by the user k can be expressed asfollows:

SINRDLk

=wHk E

{∣∣√Pk∑Lkl=1 αDkl(t)aD(θkl)sk(t−τkl)∣∣2}wk

E

{∣∣∣∣∑K−1i=1i �=k

wHi√Pi∑Lk

l=1 αDkl(t)aD

(θkl)si(t−τkl

)+nk(t)

∣∣∣∣2} .

(5)

In contrast to the uplink, a complete user separationcan be performed in the downlink direction. Because ofthat, in the proposed downlink structure shown in Figure 15,single-user weight vectors calculated in the uplink are ap-plied as transmit beamforming weights to each user sepa-rately. Therefore, downlink beamforming is much simplerthan the uplink one since the adaptive weight calculation isnot required.

However, demodulation of data and control bits for eachuser is required, and downlink beamforming is applied atthe bit level. This fact results in a considerable reduction inthe computational load, as far as the multiplier submoduleis concerned, in comparison to the equivalent module forthe uplink. Moreover, a total cancelation of interferences isachieved thanks to the individual user separation. Nonethe-less, this scheme increases the complexity of the modulationand demodulationmodule, as it must be performed for everyuser in every element of the antenna array.

In contrast to dedicated channels, broadcast informa-tion conveyed by common transport channels must be re-ceived by all the users in the cell. Therefore, the associated


From uplinkbeamformer

Uplink weights,user i

L

Downlink weights,user i

L

To L antennas

Processedsignal

Multiplierwi,1 wi,2 wi,L

· · ·

Demodulatedsignal

From base station

User i

User 1

Figure 15: Beamformer structure: downlink.

physical channels are transmitted to the whole sectorthrough one of the antenna array elements, which is equiv-alent to the radiation pattern of a conventional sectored an-tenna.

5. CODE OPTIMIZATION AND LOADDISTRIBUTION

5.1. Maximumnumber of instruction per DSP

The analog received signal is sampled in the ADC to foursamples per chip rate, obtaining packets of 1024 samples.Therefore, the number of samples per DPCCH bit can be cal-culated as follows:

Number of samples per DPCCH bit

= 1 bit · 256 chips/bit · 4 samples/bit= 1024 samples/bit.

(6)

For real-time execution, the allowable time for processingeach packet of 1024 samples is one DPCCH bit period, thatis, 66.67 microseconds.

Each of the used DSP has a performance capability of upto 2400MIPS on pipeline. Their architecture has eight highlyindependent functional units (six ALUs(arithmetic and log-ical units) of 32-/40-bits and two 16-bit multipliers). There-fore, eight 32-bit instructions per cycle can be executed. Theclock rate is 300MHz. As a result, the number of DSP cyclesfor processing one bit is

Num(cycles/bit)MAX =MIPS · Tb = 300 · 66.67µs = 20000.(7)

Therefore, the number of clock cycles in all the modulesshould be lower than 20000 cycles per bit. Table 4 shows thenumber of clock cycles per bit of each module. These valuesare always lower than the maximum number of clock cyclesper bit.

5.2. Code optimization andmodule loadFor code optimization, the following steps have been fol-lowed [28]:

(1) C-code writing;(2) obtaining a maximum instruction reduction;(3) use of DSP intrinsic operations.

The flow diagram in Figure 16 summarizes the previoussteps.

These intrinsic operations belong to a special C62xDSPLIB library. It consists of some optimized functions forfixed-point DSPs. These functions are especially used in real-time applications since their execution time is much lowerthan the C equivalent code. The main disadvantage is thatthey can only be used under certain restrictive conditions.

After code optimization and programming, module loadhas been measured. Table 5 shows the complexity of each op-timization step in clock cycles, time, and clock cycles per sec-ond for the module of coarse synchronization. As it can beseen, the complexity of coarse synchronization module hasbeen reduced two orders of magnitude after the third opti-mization step.

Table 4 illustrates the computational load of the othermodules when the three optimization steps have been ap-plied. The reduction in clock cycles of the other modules is,on average, two orders of magnitude too.

5.3. Load distribution in processorsAccording to the required computational capacity for eachmodule after code optimization, the distribution of load andtasks between DSPs must be carried out.

As presented in the Section 3, six Quads boards have beenused for signal processing, with four DSPs each. In orderto properly design the load distribution between DSPs, sev-eral questions have been considered. To begin with, it hasbeen taken into account that two independent polarizationsshould be processed, so the number of available DSPs foreach one is 12. Despite this independence, the load cannotbe divided into three Quads per polarization. This is due tothe need of five broadband receiver channels plus ADC andfive broadband transmitter channels plus DAC per polariza-tion. Receivers and transmitters boards consist of two chan-nels each, which have to be associated to two DSPs in thesame Quad. As a result, receiving and transmitting channelsmust be considered as pairs so that the possibility of usingthree independent Quads per polarization is eliminated.

Another point to take into consideration is the associa-tion of a task per DSP as far as possible. In this way, notonly the load distribution but also the data exchange be-tween DSPs is more easily understood. As regards the dataexchange between DSPs and Quads, the tasks and load distri-bution has been designed aiming at reducing the number ofdata transfers between processors as much as possible. Thismakes the interconnection between DSPs simpler since lesssynchronization for data exchange is needed. Data transfersbetween DSPs are preferred to those between Quads due tothe higher complexity of transfer and synchronization in thelast ones.


Table 4: Computational load of synchronization, demodulation, and beamforming modules.

Module Clock cycles/bit Time (µs)Million of clock cycles

per secondDSP capacityused (%)

Coarse synchronization 8700 29 131 43.5%

Fine synchronization and demodulation 6600 22 99 33%

Slot synchronization 890 3 13 4.43%

Single-user weight computation 830 3 13 4.17%

Common weight computation 5200 17 78 26%

Combiner 11500 38 174 58%

C-code writing

Compilation

Instruction measure

Achievedperformance?

True Completedprocess

False

Instruction reduction

Compilation

Instruction measure

Achievedperformance?

True Completedprocess

False

TrueMore optimization?

False

Use of intrinsic operations

Compilation

Instruction measure

False Achievedperformance?

True

Completedprocess

Figure 16: Optimization steps flow diagram.

On this basis, the scheme in Figure 17 is proposed. Asit can be observed, the synchronization and demodulationof received signals from the four antennas in the uplink areprocessed in the same Quad, hence avoiding the extra data

exchange and the difficulties of making a correct synchro-nization if the signals would be received in different Quads.Similarly, all the transmitted data are obtained and sent tothe antennas in a single Quad, for the downlink. Due tocomputational cost restrictions, only three users can be pro-cessed with this hardware implementation. A higher numberof users could be processed with more DPSs or with highercomputational capacity ones.

6. RESULTS

6.1. Simulations results

Simulations have been conducted to obtain uplink anddownlink performance results. Several aspects and charac-teristics have been varied in order to study different possibleimplementations.

Concerning adaptive algorithms, the performancesachieved with two of them have been studied. These algo-rithms are NLMS and RLS, which have been widely used inadaptive array processing applications [17]. However, due tocomputational load restrictions, only NLMS has been imple-mented in the first version of the prototype.

As it was said in the introduction, the operation ofADAM must be completely transparent to Node B. This ap-proach has an impact on the performance achieved with theadaptive beamformer. In the first group of simulations, asingle-cell scenario with a variable number of mobile usersis studied, including the effect of external interference onsystem performance. Afterwards, system-level simulation re-sults show the capacity increase obtained with ADAM, com-pared to a conventional sector antenna.

6.1.1. Uplink simulation results

As explained in Section 4.2, two cancelation schemes havebeen considered in the uplink: total interference cancelationand partial interference cancelation. Performance obtainedwith both schemes has been studied by means of simulation.

Figure 18 shows the performance achieved by both can-celation schemes when an external interference source ispresent.

As it can be observed, both array factors cancel the exter-nal interference contribution. However, if the total cancela-tion scheme is used, contributions from other mobile users


Table 5: Reduction of the number of clock cycles in the coarse synchronization module.

Module Optimization step Clock cycles/bit Time (µs) Millions of clock cycles per second

Coarse synchronization1 2571296 8571 38500

2 65732 219 986

3 8700 29 131

Quad 1: uplink, polariz 1

Fromantenna 1

RX1

Fromantenna 2

RX2

DSPSyncr user 1

Demod antenna 1all the users

DSPSyncr user 2


SDRAMFrom

antenna 3RX3

Fromantenna 4

RX4

DSPSyncr user 3


DSP


Quad 2: uplink & downlink,polariz 1 & 2

To BSTX5

To BS TX6

DSPUplink multiplier

polariz 1

WeightsDSP

Uplink multiplier

polariz 2

Chips

SDRAM

From BSRX5

From BSRX6

DSPSynchr &demod all users(downlink)polariz 1

DSPSynchr &demod all users(downlink)polariz 2

Quad 3: downlink, polariz 1

Toantenna 1

TX1

Toantenna 2

TX2

DSPWeight multipmodulation &scrambling(antenna 1)


SDRAMTo

antenna 3TX3

Toantenna 4

TX4



Raceway

Quad 4: uplink, polariz 2

Syncr user 1DSP


Syncr user 2DSP


SDRAM

Fromantenna 1

RX1

Fromantenna 2

RX2

Fromantenna 3

RX3

Fromantenna 4

RX4

Syncr user 3DSP


DSP


Quad 5: weight calculation

DSPSlot synchruplink weightcalculationpolariz 1

DSPSlot synchruplink weightcalculationpolariz 2

SDRAM

DSPDownlinkweight

calculationpolariz 1

DSPDownlinkweight

calculationpolariz 2

Quad 6: downlink, polariz 2



SDRAM



Toantenna 1

TX7

Toantenna 2

TX8

Toantenna 3

TX9

Toantenna 4

TX10

Figure 17: Load distribution.

will also be canceled, whereas with partial cancelation, a si-multaneous pointing in the directions of mobile users ap-pears as a result of the linear combination of wk. As the num-ber of users uniformly distributed within the cell is increased,the final uplink radiation pattern tends to provide a sectoredcoverage.

A simulation environment with a uniform distributionof mobile pedestrian users has been studied. Mobile speed is

3 km/h, and multipath fading is given by the two-path pro-file proposed in [2]. In (1), θkl is characterized by a Lapla-cian azimuth spectrum along with a Gaussian distributionfor each user, with an angular spread of 10◦. The numberof rays impinging on the array per user is found as a Pois-son random variable with a mean value of 25 [29]. Eachuser transmits only one data channel, with a spreading factorof 64.


5

0

−5

−10

−15

−20

−25

−30

−35−40

dB

0 20 40 60 80 100 120 140 160 180

θ (degrees)

Partial cancelationTotal cancelation

Figure 18: Normalized array factor for total and partial cancelationschemes (•: mobile users, ×: external interference).

In the simulations, a perfect power control algorithm isassumed for mobile users, that is, Pk = P, and external inter-ference power Pint is set to F dB over P.

Figure 19 shows the average uplink SINR increase ob-tained with ADAMwith respect to a typical sectored antennain two scenarios: F = −150 dB (only mobile users are presentin the cell), and one external interference with F = 20 dB. Inthe first scenario, the SINR improvement converges to 6 dBwhen the total cancelation scheme is used. With partial can-celation, ADAMwill provide the same performance as the in-dividual sectored antenna. However, in the second scenario,the partial cancelation scheme outperforms the sectored an-tenna in more than 5 dB.

As it can be observed in Figure 19, RLS provides betterperformance than NLMS, although their behavior convergesas the number of users increases. In the case of F = 20 dB, thedifference between both algorithms is mainly due to the factthat RLS provides a higher cancelation level for the externalinterference source.

Table 6 shows the reduction of the interference power inthe array output as a function of F. It can be observed thatas F increases, both algorithms provide a more significantinterference reduction. The inclusion of the strong externalinterference produces a spatial coloured covariance matrixbecause the most significant part of interfering power is con-centrated around the same angular direction. As a conse-quence, when the number of users increases and F is reduced,interference is uniformly distributed in the cell, and the levelof interference cancelation is low.

6.1.2. Downlink simulation results

In the downlink, only the total interference cancelationscheme has been considered. Figure 20 shows the averageSINR increase experimented by the mobile user when the

25

20

15

10

5

0

−5

∆SINR(dB)

1 2 3 4 5 6 7 8 9 10

Number of users

NLMS total cancelationNLMS partial cancelationRLS total cancelationRLS partial cancelation

(a)

45

40

35

30

25

20

15

10

5

0

∆SINR(dB)

1 2 3 4 5 6 7 8 9 10

Number of mobile users

NLMS total cancelationNLMS partial cancelationRLS total cancelationRLS partial cancelation

(b)

Figure 19: Uplink SINR increase as a function of the number ofusers. (a) F = −150 dB. (b) F = 20 dB.

proposed downlink beamforming algorithm is used. In thedownlink, NLMS provides a higher SINR increase than RLSbecause the improvement obtained with RLS is mainly dueto the cancelation of the external interfering source, whichdoes not influence downlink performance.

In Figures 19 and 20, the performance is studied for upto ten mobile users in the system. However, and as it was


Table 6: Cancelation level of the external interference source with the partial cancelation scheme.

F (dB) 10 log10(Pint/P

) Interference cancelation level (dB) 10 log10(|wHaU(θint)|2)K = 3 users K = 10 users

NLMS RLS NLMS RLS

−150 −5.71 −5.52 −5.36 −6.380 −9.29 −24.36 −7.83 −8.5810 −15.47 −37.19 −10.31 −13.0220 −23.58 −50.83 −16.00 −24.5430 −23.88 −60.53 −19.53 −40.83

10

9

8

7

6

5

4

3

2

1

0

∆SINR(dB)

1 2 3 4 5 6 7 8 9 10

Number of users

NLMSRLS

(a)

10

9

8

7

6

5

4

3

2

1

0

∆SINR(dB)

1 2 3 4 5 6 7 8 9 10

Number of users

NLMSRLS

(b)

Figure 20: Downlink SINR increase seen by the mobile user. (a) F = −150 dB. (b) F = 20 dB.

explained in Section 5.3, available processing hardware al-lows for processing a maximum of three mobile users. Aswell, due to the demanding computational load required bythe RLS algorithm, the beamforming process is controlled byNLMS. From the above results, it can be concluded that, for asituation with three users and an external interfering sourceof F = 20 dB, ADAM prototype provides an SINR increase of12.5 and 6.5 dB over a conventional sectored antenna in theuplink and downlink, respectively.

6.1.3. Performance in a typical user scenario

In contrast to existing 2G networks, which are dominatedby voice traffic, UMTS networks will provide a mixtureof voice and data services with different specifications ofbit rate and quality of service [30]. In addition, unlikevoice, most data services are asymmetric in nature, mean-ing that people download more information than they send.This is typical of web browsing and streaming media ser-vices.

In order to model the mixed-service and asymmet-ric characteristics of UMTS networks, three different sub-scriber profiles are considered. The first one corresponds toa conventional voice service, with a symmetric bit rate of12.2 kbps. The second group of subscribers deals with anasymmetric data service of 12.2/64 kbps, and the third groupdemands a 12.2/144 kbps asymmetric data service. In theseconditions, the base-to-mobile link will limit the capacity ofthe system. However, this limitation can be overcome usingADAM prototype thanks to the total interference cancelationachieved in the downlink.

The actual capacity increase achieved using the ADAMprototype must be estimated through system-level simula-tions. A scenario with 19 sites and 57 sectors is considered.The distance between adjacent sites is 3000m. In the simula-tions, 2000 users have been uniformly distributed within theregion of interest. Regarding the service distribution, 1000subscribers demand a voice service, 500 users demand thelow bit rate data service, and the other 500 users demand a


6000

4000

2000

0

−2000

−4000

−6000

y(m)

−6000 −4000 −2000 0 2000 4000 6000x(m)

(kbps)

3500

3000

2500

2000

1500

1000

500

0

(a)

6000

4000

2000

0

−2000

−4000

−6000

y(m)

−6000 −4000 −2000 0 2000 4000 6000x(m)

(kbps)

3500

3000

2500

2000

1500

1000

500

0

(b)

Figure 21: Total throughput per cell in the downlink (�: base station site). (a) Sector antenna. (b) ADAM prototype.

(a) (b)

Figure 22: Constellation of demodulated symbols with phase and frequency offset. (a) ω0 = 0, ϕ0 = 5◦. (b) ω0 = 8.5Hz, ϕ0 = 5◦.

high bit rate data service. Simulations have been performedwith the network planning tool presented in [31], comple-mented with the incorporation of smart antennas in the sce-nario.

Figure 21 shows the total throughput per cell in thedownlink. Using the ADAM prototype, the throughput is in-creased by a factor of 2 in each sector, in relation to the situa-tion with sector antennas. This capacity increase comes fromthe lower number of users put to outage when the adaptiveantenna is used.

6.2. Implementation results

One of the most important effects for the implementation isthe carrier frequency error between transmitter and receiversignals. In general, the carrier error ϕ(n) consists of two com-

ponents [32]:

ϕ(n) = ω0nT + ϕ0, (8)

where ω0 is the frequency offset, ϕ0 is the constant phase off-set, and T is the symbol period.

The phase offset stays constant during the reception sothat it can be compensated during the set-up stage. In con-trast, the frequency offset produces themost damaging effect.Depending on its value, the total error ϕ0(n) varies faster.This effect is corrected modifying the received symbol phaseto ±90 degrees since all the DPCCH symbol information istransmitted through Q channel.

The constellation of demodulated DPCCH symbols inthe uplink is shown in Figure 22. Firstly, (a) illustrates a


Figure 23: The constellation of demodulated symbols after phaseerror compensation.

situation where the phase error is 5 degrees and the frequencyerror is zero. In contrast, when the frequency error is notzero (Figure 22b) and no correction algorithm is applied, thephase of the received signal rotates 2π radians each 1765 bits,that is, 117.6726 milliseconds equivalent to 11 radio frames.

Figure 23 shows the constellation after the phase correc-tion algorithm has been applied. The phases of the sym-bols are not exactly 90 degrees because the implementa-tion of the phase correction algorithm has been realizedwith finite resolution (sine and cosine tabulated functions).In particular, a 30◦ resolution is considered in Figure 23so that the maximum phase error is 30◦. If the functionswere implemented with a finer resolution, the uncertaintywould be smaller. A resolution increment of the algorithmdoes not imply higher complexity, but it would requiremore memory size since more data must be stored. De-pending on the memory resources, more precision could beadded.

Eye diagrams are another representation for analyzingquality of demodulated symbols. Figures 24a and 24b showthe eye diagrams of noncorrected and corrected received sig-nal, respectively. The received signal can be sampled better inthe corrected case due to the improvement achieved with thephase compensation algorithm.

As an example of modulation and beamforming perfor-mance, Figure 25 shows the IF spectrum (44MHz) in theoutput of the broadband transmitters (uplink and downlinkpaths). As it can be seen, the spectrum corresponds to thatof a W-CDMA signal, proving the transparent operation ofADAM. This signal is then upconverted to the UMTS bandin the RF stages, and transferred to Node B (uplink) or to theantenna array (downlink).

Finally, the synthesized radiation pattern is plotted inFigure 26. Calculated weights are transferred in real timefrom the DSP blocks to the monitor-PC using LabView. Thisapplication is used in themonitoring stage to test the final ra-diation patterns in uplink and downlink, and to control the

(a)

(b)

Figure 24: Eye diagrams of demodulated symbols. (a) Withoutphase correction. (b) With phase correction.

correct performance of the overall system, from RF parame-ters to SINR.

Currently, integration tests and measurements of ADAMprototype are being performed in order to characterize thebehaviour of the complete system (antenna array, RF-to-IFchains, and DSP stages) in uplink and downlink.

7. CONCLUSIONS

A novel and real implementation of an adaptive antenna pro-totype for UMTS has been presented. Its main features areflexibility, modularity, and transparency in operation, whichmake it suitable to be connected to any existing Node B, re-gardless of whether it is prepared to work with an adaptiveantenna or not. The focus of the contribution has been on theimplementation aspects of the digital signal processing stages


Figure 25: IF spectrum (uplink path).

Figure 26: Synthesized beamformed radiation patterns (•: mobileusers, ×: external interference). Downlink diagram is shown foruser in azimuth 70◦.

(modem, synchronization, and beamforming), although anoverall description of the system has been presented.

Simulation and measurement results show the feasibil-ity and performance achieved, which outperforms the capac-ity obtained with a typical sectored antenna, especially in amixed-service scenario. In the case of asymmetric traffic sce-narios, ADAM prototype provides in average a 100% totalthroughput increase per sector in comparison with the per-formance of a conventional sectored antenna, as it is shownin the results of Section 6.1.3.

Further research work is currently being done on integra-tion with RF stages. Also, the analysis and definition of mea-surement procedures are currently being carried out. These

procedures will provide a characterization of ADAM perfor-mance in anechoic chambers and outdoor environments inconnection with real cellular base stations. Moreover, a sim-ilar prototype for global system for mobile communications(GSM) standard (under the framework of Enhanced-GSMadaptiVe Antenna (EVA) project) is currently under develop-ment because our final objective is the design of a dual smartantenna for UMTS and GSM systems.

ACKNOWLEDGMENTS

The authors wish to thank Dragados Sistemas, S.A., andVodafone for their support in the development of the ADAMproject. They would also like to thank the Spanish Ministryof Science and Technology (MCYT) for the financial supportof the MAIN project and all the professors and researchersinvolved in the ADAM project, especially, Professor ManuelSierra-Pérez.

REFERENCES

[1] L. C. Godara, “Application of antenna arrays to mobile com-munications. I. Performance improvement, feasibility, andsystem considerations,” Proceedings of the IEEE, vol. 85, no.7, pp. 1031–1060, 1997.

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[3] A. O. Boukalov and S.-G. Häggman, “System aspects ofsmart-antenna technology in cellular wireless communica-tions—an overview,” IEEE Transactions on Microwave Theoryand Techniques, vol. 48, no. 6, pp. 919–929, 2000.

[4] M. Beach, C. Simmonds, P. Howard, and P. Darwood, “Euro-pean smart antenna test-bed—field trial results,” IEICE Trans-actions on Communications, vol. E84-B, no. 9, pp. 2348–2356,2001.

[5] R. Arnott and S. Ponnekanti, “Experimental investigation ofadaptive antennas in a real DCS-1800 mobile network,” inProc. 3rd ACTSMobile Communications Summit, pp. 535–543,Rhodes, Greece, June 1998.

[6] ArrayComm Corporation, www.arraycomm.com.[7] Kyocera Corporation, www.kyocera.com.[8] S. Anderson, B. Hagerman, H. Dam, et al., “Adaptive anten-

nas for GSM and TDMA systems,” IEEE Personal Communi-cations, vol. 6, no. 3, pp. 74–86, 1999.

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temas de Comunicaciones Móviles de 3a Generación (UMTS),Fundación Airtel Vodafone, Madrid, Spain, 2002.

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[31] J. Laiho, A. Wacker, and T. Novosad, Eds., Radio NetworkPlanning and Optimisation for UMTS, John Wiley & Sons,Chichester, UK, 2002.

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Ramón Martı́nez Rodrı́guez-Osorio wasborn in Madrid, Spain, in 1975. He receivedthe Ingeniero de Telecomunicación degreein 1999 from the E.T.S.I. Telecomunicación,Universidad Politécnica de Madrid (UPM).In 1999, he joined the Departamento deSeñales, Sistemas y Radiocomunicaciones atthe same University, where he works as As-sistant Professor and is currently workingtowards the Doctor Ingeniero de Telecomu-nicación (Ph.D.) degree. He participated in COST286 (EuropeanProject), working on the simulation of communication networks,and in other public and private funding research projects. He hasalso been involved in several other research projects related toDSP-based modems and smart antennas for UMTS, and in the

development simulation platforms for mobile communicationssystems. He has also participated in field measurement campaignsin actual TV interference scenarios. His other research activitiesfocus on the area of adaptive beamformer implementation forUMTS, and on the impulsive noise interference modelling andchannel emulation, studying its impact on communications sys-tems. He has published chapters in two books, and he has also con-tributed in a number of international conferences and journals.

Laura Garcı́a Garcı́a was born in Toledo,Spain, in 1978. She received the Inge-niero de Telecomunicación degree in 2001from the E.T.S.I. Telecomunicación, Uni-versidad Politécnica de Madrid (UPM). Inthat year, she joined the Departamento deSeñales, Sistemas y Radiocomunicaciones atthe same University, where she is currentlyworking towards the Doctor Ingeniero deTelecomunicación (Ph.D.) degree. She hasworked in several research projects related to smart antennas andtheir DSP-based implementation. Her other research interests arein the study and implementation of efficient algorithms for smartantennas, specially focused on DSP-based real-time systems.

Alberto Martı́nez Ollero was born inMadrid, Spain, in 1978. He received the In-geniero de Telecomunicación degree fromthe E.T.S.I. Telecomunicación, UniversidadPolitécnica de Madrid (UPM) in 2002. Inthat year, he joined the Departamento deSeñales, Sistemas y Radiocomunicaciones,UPM, where he is currently working in sev-eral research projects. His interests includethe development of signal processing algo-rithms for smart antennas andW-CDMA systems, implementationof software radio modules on DSP platforms for real-time applica-tions and, antenna design for UMTS and DVB-S systems.

Francisco Javier Garcı́a-Madrid Velázquezwas born in Ciudad Real, Spain, on May7, 1979. He received the Ingeniero de Tele-comunicación degree from the UniversidadPolitécnica de Madrid (UPM) in 2002. Inthat year, he joined the Departamento deSeñales, Sistemas y Radiocomunicaciones,UPM, where he is currently working towardthe Ph.D. degree at the Escuela Técnica Su-perior de Ingenieros de Telecomunicación(E.T.S.I Telecomunicación). His research interests include the im-plementation of signal processing algorithms for smart antennas,studies of impulsive noise effects in communications systems, andfuzzy logic applied to channel estimation and beamforming.

Leandro de Haro Ariet received the Inge-niero de Telecomunicación degree in 1986and the Doctor Ingeniero de Telecomuni-cación degree (Apto cum laude) in 1992,both from the E.T.S.I. Telecomunicación,Universidad Politécnica de Madrid (UPM)(Departamento de Señales, Sistemas y Ra-diocomunicaciones). Since 1990, he has de-veloped his professional career in the De-partamento de Señales, Sistemas y Radio-comunicaciones as Profesor Titular de Universidad in the signal

http://wireless.ece.ufl.edu/eel6503/http://wireless.ece.ufl.edu/eel6503/file:www.moyano.comfile:www.pentek.comfile:www.mc.comfile:www.ti.com


theory and communications area. His research activity coversthe following topics: antenna design for satellite communications(earth stations and satellite on board); study and design of satel-lite communication systems; and study and design of digital TVcommunication systems. He has been actively involved in severalofficial projects and with private companies (national and inter-national). He has also been involved in several European projects(RACE, ACTS, COST). The results of his research activity may befound in several presentations in national and international confer-ences as well as in published papers.

Miguel Calvo Ramón was born in Pueyode Jaca, Huesca, Spain, on June 10, 1949.He received the Ingeniero de Telecomuni-cación degree from the E.T.S.I. Telecomuni-cación, Universidad Politécnica de Madrid(UPM) in 1974 and the Doctor Ingeniero deTelecomunicación degree (PhD) from thesame University in 1979. He works as a Cat-edrático (Full Professor) in the Departa-mento de Señales Sistemas y Radiocomuni-caciones since 1986. Since his incorporation to UPM in 1974, hehas worked in a number of projects related to numerical meth-ods in electromagnetics, EMC, antennas and testbed simulators forsatellite communications (Hispasat). He has worked in the coor-dination procedures of the Spanish Hispasat satellite system withIntelsat, and part-time as a Technical Director of the Space Divi-sion at RYMSA. He has also worked as an Evaluator of Proposals inthe framework of the European IST program. He was a ResearchVisitor at Queen Mary College, London University, in 1983 andTechnical Visitor at Nichols Centre, Kansas University in Lawrence,in 1993. He has coauthored a number of papers in technical jour-nals and contributed in a number of international conferences. Hewrote a chapter in the book Reflector and Lens Antennas: Analy-sis and Design Using Personal Computers (C. J. Sletten, Ed., ArtechHouse Publishers, 1988).

1. INTRODUCTION2. UMTS SMART ANTENNA ARCHITECTURE AND OPERATION OF ADAM PROTOTYPE3. HARDWARE ARCHITECTURE4. PRINCIPLES AND IMPLEMENTATION OF SOFTWARE RADIO MODULES4.1. Set-up, synchronization, and MODEM stages4.1.1. Set-up procedure4.1.2. Synchronization algorithms4.1.2.1. Coarse synchronization4.1.2.2. Fine synchronization

4.1.3. Demodulation in uplink and downlink4.2. Adaptive beamformer4.2.1. Uplink operation and implementation4.2.2. Downlink operation and implementation

5. CODE OPTIMIZATION AND LOAD DISTRIBUTION5.1. Maximum number of instruction per DSP5.2. Code optimization and module load5.3. Load distribution in processors

6. RESULTS6.1. Simulations results6.1.1. Uplink simulation results6.1.2. Downlink simulation results6.1.3. Performance in a typical user scenario

6.2. Implementation results

7. CONCLUSIONSACKNOWLEDGMENTSREFERENCES

adam:arealisticimplementationforaw-cdma smartantenna...adam: a realistic implementation for a w-cdma...

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