TH3C-4

E-Valuation of Synchronization andl Fractilonally Spaced Equalilzat'ionin a MIMO SC-FDE Test-Bed

Qipeng Cai', Andreas Wilzeck' and Thomas Kaiser'

'University Duisburg-Essen, Department of Communication Systems,Bismarckstrasse 81, 47048 Duisburg, Germany; Email: {qipeng.cai, andreas.wilzeck}@uni-DuE.de

2mimoOn GmbH, Technologiezentrum fur Duisburg,Bismarckstrasse 120, 47057 Duisburg, Germany, Email: info@rmimoOn.de

Abstract- This contribution deals with the synchronizationand the fractionally spaced equalization for Multiple-InputMultiple-Output (MIMO) Single-Carrier (SC) systems withFrequency Domain Equalization (FDE). Synchronization, chan-nel estimation and the proposed equalization are evaluated asa system employing real-world over-air transmission in the2.4 GHz ISM-band by using our current MIMO test-bed [1].Spatial multiplexing is demonstrated, allowing the system toreach an un-coded net data rate of 28.78 Mbit/s using 8-PSKmodulation and a 3-dB bandwidth of 10 MHz.

Index Terins Multiple-Input Multiple Output, Single-Carrier, Frequency Domain Equalization, Synchronization.

I. INTRODUCTION

Inter Symbol Interference (ISI) caused by the time dis-persion property of multi-path propagation has one of thehighest impacts on the speed of high data rate wirelesstransmission. Orthogonal Frequency Division Multiplexing(OFDM), which has been widely deployed in current Wire-less Local Area Network (WLAN) standards, is inventedto overcome the above mentioned difficulty with reasonabledigital signal processing complexity. However, an OFDMsystem must fulfill rigorous requirements on timing andfrequency synchronization [2], otherwise the orthogonalityamong the sub-carriers is lost and the communication systemsuffers from ISI and Inter Carrier Interference (ICI). Anotherweak point of OFDM systems is the high Peak-to-AveragePower Ratio (PAPR) of its signal, which impacts greatlyon the power efficienLcy of the power amplifier and thenlimits the battery lifetime of mobile devices. Single Carriertransmission with Frequency Domain Equalization (SC-FDE) is viewed as an alternative approach to an OFDMsystem It uses a concept similar to that of an OFDM systemto convert the linear convolution of the transmitted signaland channel impulse response into circular convolution inthe discrete domain with the help of a Cyclic Prefix (CP) ora Unique Word (UW). The benefits of SC-FDE are easedsynchronization requirements and its low PAPR propertysignal, which are very advantageous to low cost portabledevices. Thus SC-FDE is a top candidate for the uplinkin the physical layer of next generation wireless communication systems, for instance in the IEEE 802.16 WirelessMetropolitan Area Network (WMAN) and 3GPP Long Term

Evolution of Universal Mobile Telecommunications System(UMTS-LTE).

Another attractive technology expected to be exploited innext generation wireless communication systems is MIMOtechnology, which is able to increase channel capacity bydeploying several transmit and receive antennas withoutincreasing the signal bandwidth or the Signal to NoiseRatio (SNR). In order to offer high data-rate communica-tion, the MIMO technology attracts not only the academiccommunity, but also standardization bodies in the industrialcommunity.

Practical studies are a crucial part of the developmentof complex systems like wireless communication systems.The hardware (Hw) platforms can be differentiated intothree kinds, test-beds, demonstrators and prototypes. Asmentioned in [3] a test-bed or measurement device is ableto give access to real channel data with or even withouttypical hardware effects. Typically no real-time processing isperformed on the Hw platform itself. A demonstrator is usedto demonstrate some functionality, for example synchroniza-tion. A prototype is usually a complete set of functionalitiesoperating already in real-time such as a first ApplicationSpecific Integrated Circuit (ASIC) implementation.

In this contribution we will evaluate a 2 x 2 MIMOSC-FDE system employing spatial multiplexing based onour flexible MIMO prototyping test-bed [1]. The tasks andstrategies for synchronization in time and frequency, equal-ization and the frame structure are described. A modifiedSchmidl & Cox metric [4] is adopted to achieve coarsetiming synchronization. The Unique Word technique is cho-sen to ease residual frequency offset estimation and residualphase tracking [5]. Fractionally spaced frequency domainequalization [6] is deplo ed to omit a fine s nchronizationof the system.

II TEST-BED & MEASUREMENT SETUP

A detailed overview of our 4 x 2 MIMO test-bed canbe found in [ I]. Here we would like to simply pointout the most important features. The test-bed is based onSundance's signal processing platform, which is modular,scalable and re-usable for different purposes. The test-bedis an advanced of/ine or hardware in the loop system for

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Tx 1

Binrydata1 ingI-1u-Y RRC IVI j jnaftng -(OR2 Digital IF Mod.

Multiplexer STARS^2

IPreamble rm&mae Transmitter Ta 2

ITraining Seq (OCRS2) Digital IF Md.

Fig. 1. Block Diagram of 2 2 MIMO SC-FDE Transmitter

Fig. 2. Block Diagraimn of 2 x 2 MIMO SC-FDE Receiver

the 2.4 GHz ISM band. The function can be describedas follows: pre-processed digital base-band signals are firstdigitally up-converted to a low Intermediate Frequency (IF).These IF signals are then converted by Digital-to-Analog(D/A) converters into analog signals and then further up-converted to the 2.4 GHz ISM-band by an analog MIMORadio Frequency (RF) front-end. The RF signals are radiatedby the two transmitting antennas. At the receiver-side RFsignals are received with four antennas and are at first down-converted to a low IF by the analog MIMO RF front-end. Allfour received low IF signals are synchronously sampled byAnalog-to-Digital (A/D) converters, and the resulting data islogged in real-time by huge memory modules. The memorysize is 2 x 1 GByte, resulting in a logging memory of 512MByte per antenna. The maximum sampling rate is 100Mega Samples Per Second (MSPS), and the resolution is14 bit.For our evaluation we will use a 2 x 2 setup of the test-

bed. The RF carrier frequency is 2.46 GHz and the IF is 15MHz. Both the A/D conversions and the D/A conversions,are operating with 40 MHz. The transmit signals have a 3-dB signal band-width of 10 MHz. No reference cables orsimilar are shared between the transmitter and the receiver.which means that they are fully independent systems.

III MATLAB IMPLEMENTION OF MIMO SC-FDE

A. Overview

The digital low IF signals for transmissio are generatedby a MATLAB implementation of the MIMO SC-FDEtransmitter, which employs spatial multiplexing. The blockdiagram can be seen from Fig. 1. The binary data is firstmapped to symbols employing 8-PSK data modulation. Thissymbol stream is separated into blocks of 48 symbols and aUnique Word (UW) of Lcp = 16 symbols is added at thebeginning of each block. Therefore, the IFFT/FFT block sizeis 64 symbols. The addition of a Cyclic Prefix (CP), or inthis case a UW, is required to avoid Inter Block Interference

(IBI) and to fulfill the theorem of cyclic convolution. Thelength Lcp should be as long as the length of the channelimpulse response plus the group delays of all the filters inthe transmitter and the receiver.

TX AAAAAAAA| Chu ° Chu 0 | B, > B2 >-BC

Tx 0 0 0 hTX2 L h h a2 2 E--N

Preamble *-< Trainingsequences Data playload blocks(TDM)

Sub-frame

Fig. 3. Frame Structure of Our MIMO SC-FDE System.

Corresponding to a special frame/sub-frame structure theresulting blocks are multiplexed to the two transmitterbranches, and preambles for synchronization and channelestimation are added. For our 2 x 2 MIMO SC-FDE demon-stration a frame structure as given in Fig. 3 is proposed,which consists of a preamble and several sub-frames. EightFrank-Zadoff sequences with 16-symbol length constitutethe preamble, which is used to obtain frame synchronizationand coarse timing synchronization. Each sub-frame startswith training sequences assisting the MIMO channel estima-tion and frequency offset estimation. The training sequencesof each transmit antenna are chosen in such a way thatthey are orthogonal in time. The training sequences for eachtransmit antenna are two repeated Chu sequences, each witha length of 64 symbols and an added Cyclic Prefix (CP) oflength Lcp. Several data payload blocks follow this sub-frame training.

Root Raised Cosine (RRC) filters with a roll-off factoroft = 0.3 are carrying out pulse shaping and are limitingthe bandwidth of the base-band signals. These filters areoperating at an over-sampling factor of 2. Afterwards a dig-ital quadrature modulation converts the base-band signals todigital low IF signals, which requires further over-samplingto reach the D/A conversion rate. The digital low IF signalsare converted into analog signals and then further trans-formed into RF signals. Thereby, several filtering, mixingand amplification processes are involved. Finally RF signalsare broadcast over the air.The RF signals are received by the receiver (Fig. 2) via its

antennas, filtered by band-pass filters, down-converted fromradio frequency to IF frequency by mixers and amplifiedby low-noise amplifiers. Next, A/D converters change theanalog IF signals into digital IF signals, which are passed tothe digital processing functions implemented in MATLAB.A digital quadrature demodulation is performed to obtain

complex base-band signals. Afterwards RRC filters are de-ployed to limit the noise bandwidth to the system signalbandwidth. They also act as matched filters for the transmitpulse shape.

Time and frequency synchronization functions have to hecarried out to achieve the frame detection and to estimatethe frequency offset caused by the independently runningreceiver and transmitter local oscillators. The compensation

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for the carrier frequency offset is performed for the wholeframe. The channel estimation is carried out afterwards, andthe channel transfer functions of the MIMO channels are es-timated under the assumption of quasi-static channels. Basedon this estimate, a fractionally spaced MIMO FrequencyDomain Equalizer (FDE) resolves the superposition of theMIMO channel, compensates fractional delays, and removesthe distortion caused by the multi-path channel.

Instead of a CP, we used a UW prefixed to each block.This UW is still included after equalization of a block andcan therefore be used to analyze the behaviour of the FDE(quality measures), to estimate a residual frequency offset,which can be also caused by sampling offsets as well, andfor phase tracking purposes. Afterwards the UW needs to beremoved. We implemented phase tracking after the equalizerin order improve the received signal constellation prior to thedetection (de-mapper). At the end the received and equalizedsymbol streams are de-multiplexed, detected and translatedinto binary data, which can be compared to the transmitteddata.

B. SynchronizationFrame start position, frequency offset and phase offset

within the data transmission duration as well as the channelimpulse responses should be detected and estimated in thebase-band signal processing of the receiver. For frame startdetection we choose the metric proposed by Schmidl & Coxin [4], but we extend it to fit our preamble with 8 shortFrank-Zadoff sequences.

The k-th received symbols of the i'-th sequence can beexpressed by a vector as

rik = [r(k+ (i-I)L). r(k+iL-1)]T i = I,2.

The proposed metric can be formulated as

k r-Lk ri,k c r,k2)i=2 i=2 /

A property of the Discrete Fourier Transform (DFT) isthat a cyclic shift of the sequence will only result in aconstant phase rotation which can be easily compensated viaequalization. Thereby, the constraint for choosing an optimalstart point for the DFT window is greatly relaxed. The DFTwindow only needs to be placed in such a way that the datablock used for the DFT process will not be smeared by thedispersion from a previous block. Under this requirementwe can almost arbitrarily choose a start position and whilebeing more concerned with robustness

The modified Schmidl & Cox metric for frame detectiongenerates a plateau which corresponds to the position of theoptimal window with some constant offset. It is intuitive toselect the central position of the plateau as the starting pointof the DFT process. Unfortunately, there is an uncertaintyconcerning the optimal position caused by the frequencyselective channel and the noise. A longer impulse responseof the channel yields a narrower optimal window of possible

start positions and the slopes of the left and right sides of theplateau are getting lower. Thus, it is difficult to get a reliableresult by selecting the central position of the plateau whenthe channel length grows near the length Lcp. Hence, wechoose an additional algorithm in order to get a more reliableresult, which is based on proposals in [7]. Details will beshown in an upcoming contribution.A carrier frequency offset can significantly degrade the

receiver performance. The offset causes a continuous rota-tion of the base-band signal constellation and must thereforebe cormpensated for correct detection of the data. In order toestimate the carrier frequency offset, the repetition propertyof the training sequences can be employed to estimate thefrequency offset in the received base-band signal.

Corresponding to the proposed frame structure, the train-ing sequence of each transmitter antenna consists of tworepeated Chu sequences of length LChu = 64 symbols,as shown in Fig. 3. Each Chu sequence is protected by aCP of length Lcp. So the period of the repeated symbolsin these two Chu sequences, Lp = LChu + Lcp, is 80symbols. Under the assumption that the channel length isnot above the length of the CP and that the channel isconstant during frame transmission, meanwhile neglectingthe frequency offset and the noise, the received symbols inthe two repeated Chu sequence have the relationship as

r[k + Lp] = r[k3, k = 0(1)LCh,-1f (3)

With Af, the frequency offset, (3) is changed to

r[k + Lp] = r[k] eJ2,TAfLpT,, k = O()Lchu- 1, (4)

where Ts denotes the symbol duration.Hence, the estimate of Af from symbol k can be expressedas

Afk =.._.JLI aru(r [k] r[k+Lp]), k = O(I)LChu-1,(5)

where arg(Q) is the operator to get the angle information ofa complex value, r[k] are the received symbols of the Chusequence and * denotes complex conjugate.A more robust estimator can be constructed through

averaging Afk over the whole Chu sequence as

1 LCh, i

A = Ch E AJkk=O

(6)

In order to cope with residual offsets and possible vari-ations of the phase, we implemented a phase trackingalgorithm based on the UW approach as given in [5] (method3).

C. EqualiationFractionally Spaced Equalization (FSE) [6] is motivated

hy bandwidth expansion by the factor (1 + a) caused by theRoot Raised Cosine (RRC) filters with a roll off factor ofa, which are typi ally deployed in communication systems.This bandwidth expansion requests an over-sampling factor

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of two in the digital processing. The fractionally spacedequalizer performs equalization in such away that a fore-going fine synchronization is not required, which simplifiesthe synchronization requirements. The input of the FSE isover-sampled by a factor of two, while the output is onsymbol rate.

In the case of a MIMO SC-FDE system with NT transmitantennas and NRF receive antennas a MIMO FSE of an singleblock can be described by

d = NT X (FN D)] W JIN,' 9 (F2NGRRC) r (7)

whereda d dNT]T is an NTx1 Ivector, containingthe equalized data vector of the transmitted streams, I is thez x z identity matrix, FN the N X N Fourier matrix, D thefrequency domain down -sampling matrix of size N X 2:N,Wis the 2NNT X 2NNR equalizer matrix, GRRC is the 2N x2N RRC filter matrix. 'r is a 2NNR x 1 vector containing thestacked received vectors, where the cylic prefix is alreadyremoved. We formulate it as

Constellation Diagram

1.5

1-

0.5k-

0F-

-0.5k-

-1.5

-2-2 -1 0 1 2

Re {d}

Fig. 4. Constellation of Equalized Received Symbols.r = (INR (8 PremT)rC=(INR Prer)

LH(INT (GRRCPadd))d + v (8)

where Prem, and Padd denote CP removal matrix and CP in-sertion matrix respectively, GRRC = F2 GRRCF2N signi-fies the linear RRC convolution matrix, d = Id1 ? dNT]is the stacked transmit data vector, and v = IVI * VNR ]T

is the stacked receive noise vector.The MIMO channel estimation procedure is based on a

Least Square (LS) approach and makes use of the timeorthogonal training sequences offered at the start of eachsub-frame. For the later measurements a Zero Forcing (ZF)MIMO equalizer is used, which simply inverts the MIMOchannel effects, but comes at the cost of noise amplification.

IV. RESULT & CONCLUSION

The transmitter and the receiver are placed in two differentsized office rooms, which are separated by a wall. Thereby,the measurement is performed in an indoor environmentwithout Line of Sight (LoS) path. The constellation dia-gram of the received de-multiplexed symbols is shown inFig. 4. The measurement is based on 172 sub-frames, eachcontaining 10 blocks. Each block has a data payload of 48symbols, which are 8-PSK modulated. The reached un-codednet data rate (excluding trainlngg) of this 2 x 2 MIMO SC-FDE transmission employing 8-PSK modulation and a 3-dbbandwidth of 10 MHz can be calculated to 28.78 Mbit/s

Within this contribution we evaluated synchronizationalgorithms combined with fractionally spaced equalization

for a MIMO SC FDE system employing spatial multiplexingunder realistic conditions. We proposed a simple to use

frame structure which can of course be further improved,but allows an easy access for the estimation of channel

parameters.

The analog RF hardware of the current test-bed will beupdated within the next few months. The aim is to reach a

better performance and to have access to the 5 GHz ISM-band as well.

V. ACKNOWLEDGEMENT

This work is partly funded by the Deutsche Forschungs-gemeimnschaft (DFG) under the project title "Analytische undexperimentelle Untersuchung von mehrteilnehmerfahigenMehrantennen-Systemen mit niederratiger Ruckkoppelung"(KA 1154/15).

REFERENCES

[1] A. Wilzeck, M. ElL-Hadidy, Q. Cai, M. Amelingmeyer, and T. Kaiser,"MIMO Prototyping Test-bed with Off-The-Shelf Plug-In RF Hard-ware," IEEE Workshop on Smart Antennas, 2006.

[2] M. Speth, S. A. Fechtel, G. Fock, and H. Meyr, "Opti-nium receiverdesign for wireless broadband systems using OFDM - Part I," IEEETransactions on Communications, vol. 47, no. 11 pp. 1668-1677,1999.

[3] T. Kaiser, A. Wilzeck, M. Berentsen, Universitaet Duisburg-Essen,Germany; M. Rupp, Technische Universitaet Wien, Austria, "PRO-TOTYPING FOR MIMO SYSTEMS - AN OVERVIEW" (invited tospecial session "MIMO Prototyping"), 12th European Signal Process-ing Conference (EUSIPCO 2004), Vienna, Austria, September 6-10,2004.

[4] T. Schmidl and D. Cox, "Robust frequency and tiiming synchroniza-tion for OFDM," IEEE Transactions on Communications, vol. 45, pp.1613 1621, 1997.

[5] M. Huemer, H. Witschnig, and J. Hausner, "Unique Word Based PhaseTracking Algorithms for SC/FDE Systems," Proc. IEEE GLOBECOM2003, pp. 70 74, 2003.

[6] P. P. Vaidyanathan, and B. Vrcelj, "Theory of fractionally spacedcyclic prefix equalizers" Proc ICASSP 2002 vol 2 pp 1277 1280,2002.

[7] S. Reinhardt, R. Weigel, "Pilot Aided Tiiming Synchronization forSC-FDE and OFDM: A Comparison," Proc. ISCIT 2004, Sapporo,Japan, October 2004.

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