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An 88 FPGA-based MIMO-OFDM Real-Time
Transmission Testbed: OGNO Implementation and
Experimental ResultsYang Lan, Zhan Zhang and Hidetoshi Kayama
DOCOMO Beijing Communications Laboratories Co., Ltd, China
Emaillan@docomolabs-beijing.com.cn
AbstractHardware testbeds are an essential tool to evaluate the
performance of multiple-input multiple-output (MIMO) systems
in a realistic environment. However, most of the existing MIMO
transmission testbeds have been conducted under less than or
equal to four antennas. An 88 field programmable gate arrays
(FGPA)-based MIMO-orthogonal frequency division
multiplexing (OFDM) real-time transmission testbed has been
developed by the DOCOMO Beijing Labs (DBL) in a typical
indoor environment. Our objective is twofold: 1) to validate the
functionality of MIMO and OFDM technologies; 2) meanwhile,
to verify our receiver detection algorithm orthogonal
grouping-based near optimal detection algorithm (OGNO),
proposed for high order MIMO systems. This paper presents a
description of the testbed, detailing the testbed architecture,
algorithms interest and hardware components. Moreover, we also
present measurement results and show the impact of spatial
correlation on system performance.
Keywords-MIMO, OFDM, Testbed
I. INTRODUCTION
Information theoretic analysis shows that multiple-inputmultiple-output (MIMO) systems can yield significantcapacity improvement when rich scattering environment isproperly exploited [1][2]. The combination of the MIMOtechniques with orthogonal frequency division multiplexing
(OFDM) for broadband systems is seen as a promising basisfor next-generation high data rate wireless systems [3]. As an
essential tool, hardware platforms and testbeds are capable toevaluate the performance of MIMO-OFDM systems inrealistic scenarios. After years of extensive theoretical studies,current literature shows quite an extensive number of MIMOtestbeds.
In [4], a 44 MIMO prototyping testbed has been
developed by a research team at Brigham Young University.This testbed uses fixed point digital signal processing (DSP)microprocessor development boards for both the transmitter &receiver stations. As the data source, a computer generates thefour data streams and passes the sampled signals to the DSPboard. The RF channel emulators are employed to modelfading channels such as Rayleigh, Ricean, and Nakagami.
Channel estimation and data detection are performed by acomputer at the receiver station. In [5] and [6], the authors
have reported a 33 testbed MIMO testbed and a real-time22 space-time coding MIMO testbed. Similar to our testbed,
OFDM technology was used to build a 22 wideband MIMOchannel system in [7] and [8]. Another 22 testbed has been
developed by Rice University in Texas [9]. The testbed isbased on a field programmable gate array (FPGA). Another
44 testbed developed by the University of Bristol [10]operates at 5 GHz and uses a DPS micropercessordevelopment board for the baseband processing. Similar to ourtestbed, each transmitter has a preamble orthogonal to all
others and the channel state information is obtained at thereceiver. But this testbed does not allow real-time transmission
since the synchronization is done offline.These testbeds mentioned above have been conducted under
less than eight antennas. In the future 4G standard, to matchthe traffic demand and the higher peak data rates,LTE-Advanced needs the high order MIMO transmissions (upto 8 antennas) [11][12]. This paper describes the design and
development of an 88 MIMO-OFDM real-time transmissiontestbed. It operates at 2.35GHz with a RF bandwidth of6.25MHz. FPGA boards are used for processing basebandsignal. Upconversion to RF is performed with eight RF vectorsignal generators (Agilent E4438C). Downconversion wasperformed by eight signal analyzers (Agilent N9020A MXA).At present, it employs 16-QAM signal modulation and the
multiplexing transmission scheme. The platform offers theverification of the proposed signal detection algorithm in [13].
Meanwhile, we also provide the measured bit error rate (BER)versus signal to noise ratio (SNR) curves in indoor MIMOpropagation environment.
The rest of the paper is organized as follows. In Section II,the detection algorithm is briefly introduced. FPGA-basedtestbed architecture is presented in Section III. The
measurement results are given in Section IV. Finally, SectionVI concludes the paper. Throughout this paper, superscript T
and H stand for matrix or vector transpose and Hermitiantranspose respectively. Vectors and matrices are representedusing bold fonts while scalars in italics.
II. R ECEIVERDETECTION ALGORITHM DESCRIPTION
Fig.1. 8h8 MIMO-OFDM system
,&633URFHHGLQJV
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7/29/2019 An 88 FPGA-based MIMO-OFDM Real-Time
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In this section, a brief overview of the 88 MIMO-OFDMsystem with the proposed detection algorithm is given. Fig.1
depicts the system structure, where the data stream ismultiplexed into 8 parallel independent OFDM modulateddata substreams, which are then transmitted by 8 transmitantennas simultaneously.At the receiver side, first the cyclicprefix (CP) is removed. After FFT operation, the receivedsignal is converted into the frequency domain. Let hi,j(k) be the
channel gain from transmit antenna i to receive antenna j atthe kth frequency bin. The MIMO channel matrix at the kthfrequency bin can be represented by
> @
T
T
khkhkh
khkhkh
khkhkh
kkkk
)()()(
)()()(
)()()(
)()()()(
8,82,81,8
8,22,21,2
8,12,11,1
821
hhhH, (1)
where hj )(k is the 8u1 column vector of channel gainsassociated with receive antenna j. The symbol from the ithtransmit antenna is denoted as si(k) at the kth frequency bin,and the corresponding transmit symbol vector as
> @Tksksksk )(,),(),()( 821 s . The additive white Gaussian
noise vector is n with variance V2. Therefore, the received
signal in the frequency domain can be expressed as,
> @ )()()()(,),(),()( 821 kkkkykykykT
nsHy . (2)
As the optimal decoding algorithm, Maximum Likelihood
Decision (MLD) rule is defined as
)||)()()((||minarg)( 2
)(
kkkkk
sHyss
:
, (3)
where: includes all Q8 possible candidate sequences. Q is the
modulation set size and ||.|| denotes the Euclidean norm. TheMLD requires an exhaustive search overQ8 candidates to findthe optimal solution. Therefore, although MLD is optimal inthe sense of minimization of bit error rate, it is impractical asits complexity increases with the number of transmitterantennas, especially for high order MIMO systems. To reducethe complexity of MLD, in [13], we proposed a new detection
method called orthogonal grouping-based near optimaldetection algorithm (OGNO) for high order MIMO systems.The OGNO uses orthogonal grouping to convert a high orderMIMO system into several lower order MIMO systems, whereeach subsystem can be viewed as one group. Using thedetection algorithm, near-ML dynamic-layer-ordering M-paths
(DOM) in [14], each group performs detection independentlyand outputs several candidate sequences having different
reliabilities. At the last step, the overall optimal sequence isobtained through ranking combination static group search. Thebrief overview of OGNO steps in detail is given in Table I. Formore details, readers are referred to the previous paper [13].
Step 1.Orthogonalgrouping
1-1. For simplicity in this illustration, we assumeN(8) transmit antennas and Nreceive antennas.Define G as the group number and K the streamnumber for each group. The subchannel matrix
is > @ GggKKgg ,...,11)1( hhH .
1-2. Define Gggg HHHHH 111 . QRD
getsgg
Hg HRQ .
1-3. gV is the matrix includes from the rowN-K+1
toNofgQ .
> @ggggg
gggg
nsHns0H0
nVHsVyVy
~~~~
~
,
whereggg HVH
~ is a KK equivalent channel
matrix and gn~ is still AWGN.
Step 2.Group
detection
2-1. Definegg
Hg HRQ
~~~ . Then we have
gqgggggggggqgnsRnQsHQyQy ~
~~~~~~~~ ,
As the group detection algorithm, DOM outputs Lcandidate sequences
> @ 21~~minarg gggqgLg
g
sRysss
:
.
Step 3.Ranking
combination staticgroupsearch
3-1. After step 2, each group generates L candidatesequences. Therefore, there are L
G sequence
combinations in total. To obtain the final estimatesequence, a ranking combination static group searchwith the reliabilities for the optimal combinationneeds to be performed.
TABLE I. Overview of OGNO steps
III. FPGA-BASED TESTBED ARCHITECTURE
Using FPGA boards, we implemented the detectionalgorithm OGNO described above into the 88 MIMO-OFDMreal-time transmission testbed. FPGA is reconfigurable andhence suitable for the rapid prototyping of MIMO
transmission schemes. The testbed system includes a
transmitter and a receiver each including 8 antennas connectedto an RF front end flowed by an up or down converter tointermediate frequency model. Meanwhile, filters are used tominimize noise before processing of an IF signal. To preventmagnetic wave from reaching areas where they would causemagnetic interference, we have built up a magnetic shieldingroom, which is a 12 m 6.7 m2.7m room with metal floor,
ceiling, and walls. It provides an effective shielding for ourequipments from ambient electromagnetic interference. Theantenna shown in Fig. 2 has dimensions 10mm20mm andomnidirectional properties. The element spacing of theantenna array is adjustable from 0.5 to 4.
Fig.2. Antenna dimension
FFT size 1024
Number of used subcarrier 896
Frame length 32 symbols
Number of transmitter antenna 8
Number of receiver antenna 8
Synchronization Perfect
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Guard Interval 15.36Ps (96 samples)OFDM Symbol Duration 163.84Ps (1024 samples)
TABLE II. System parameters
Fig.3 illustrates the testbed transmitter, which consists of
some models. 1): Channel coding. It is implemented in theXilinx VHS-AD-SX55 FPGA, which includes 8 14-bitdigital-to-analog converters (DACs), and 8 14-bit
analog-to-digital converters (ADCs). One FPGA, clocked at50MHz. 2): Two same FPGAs are for frame generation. Oneframe consists of 32 OFDM symbols. The first symbol is forAGC and the second and third symbols are forsynchronization. 8 OFDM preambles are for channelestimation and 21 OFDM data symbols.
The upconversion from the IF frequency of 300 MHz to thecarrier RF frequency of 2.385 GHz is performed by 8 AgilentE4438C signal generators and the signals are then transmittedthrough 8 dipole antennas. Because there are 8 signal
generators, the carrier coherency is an important problem. Inorder to achieve carrier coherency, equipment calledDistribution Amplifier Z5623A K05 is used. With 1 RF input
and 8 RF outputs, it enables up to eight signal generators tooperate in a phase locked and coherent mode at a commonfrequency. This will yield carrier coherency from 50 MHz to4.0 GHz. The distribution amplifier takes an oscillator signalfrom signal generator one, called master signal source,
amplifies it and distributes it to multiple other slave signalsources.
Fig.3. Testbed transmitter
Fig.4 shows the schematic diagram of the testbed receiver.At receiver, eight signal analyzers perform as downconverters
to convert the RF signals to the IF of 300 MHz. The 8downconverts employ a common local oscillator of frequency2350 MHz, in this way there exists a perfect carrier frequency
synchronism at the receiver side. The AD converters samplingfrequency is set at 80 MHz in order to obtain a 6.25 MHzreplica. For each of the eight data paths, the samples aredigitally downconverted into an inphase (I) and a quadrature(Q) component. The receiver baseband signal processing
mainly contains FFT transformation, channel estimation, QRdecomposition, group detection and ranking combinationstatic group search.
Modules Data format (bD.bF)
FFT input (16.15)
FFT output (16.15)
CIR estimation coefficients (16.15)
Digital I & Q output (16.15)
QR decomposition output (16.14)
Matrix Multiplication (16.15)
TABLE III: Summary of fixed-point data format precision at the receiver
FPGA-based implementation allows the use of a
customized fixed-point hardware definition wherein eachcoefficient and each state variable may be represented using a
different number of bits. The fixed-point format we used is16-bit word-length representation. bF indicates fractional bitsand bD represents fractional bits plus sign bit. The resolutionof the different variables is summarized in Table III.
Fig.4 Testbed receiver
IV. MEASUREMENTS
Parameters Values
Carrier frequency 2.35~2.6 GHz
MIMO configuration 88
MIMO mode Spatial Multiplexing
Detection scheme OGNO +DOM
Transmission scheme OFDM
Data modulation 16QAM
Channel coding1): No coding
2): 1/2 Turbo coding
Peak spectral efficiency
(with coding)10 bps/Hz
Peak data rate(with coding)
50Mbps
Signal bandwidth 5 MHz
Maximum clock cycle 50 MHzTABLE VI. Testbed specification
The layout of the measurement scenario is depicted in Fig. 5.The transmitter is located in one corner of the lab room andthe receiver is put in another corner diagonally across the
room.
Fig. 5 Demonstration scenario for BER measurements
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In order to check the impact of antenna spacing on the BERperformance, we set the different antenna spacing.
Case 1: Firstly, the Tx and Rx antenna array with elementspacing 4 was set at both the transmitter and receiver sides.
Case 2: 8 elements are divided 4 groups. For each group theelement spacing is 4 and 4 for group spacing at both thetransmitter and receiver sides.
Case 3: the Tx and Rx antenna array with element spacing0.5 was set at both the transmitter and receiver sides.
. . .
Fig.6 presents the performance comparison of measured
uncoded BER versus measured SNR. As a lower bound, thetheoretical optimal performance is showed. These results havebeen measured for a 16QAM constellation and no coding used,which corresponds a peak data rate of 100 Mbps. The trend
curves are obtained by averaging over these scatteredmeasured results. Wee can observe:
1): As expected, the red curve representing the BERperformance with the antennas spacing of 4 has the bestperformance among three cases.
2): Compared to the optimal performance, there is around
3dB performance loss at the BER of 10-3
. These factors thataffect the real measurement results in hardware consist ofthese channel estimation error, QR decomposition error andthe fixed point roundoff error.
3): At the BER of 10-3, there is around 3.5dB performanceloss between case 1 and 2 and the performance gap is 4dB
between case 2 and 3, which means spatial correlation doescause the performance loss.
4): Although the complexity of MMSE is low, theperformance far inferior to the proposed detection algorithm.
V. CONCLUSIONS
In this paper, an 88 FPGA-based MIMO-OFDM real-timetransmission testbed has been presented. The baseband digitalsignal processing is based on a novel MIMO detectionalgorithm: OGNO, which is able to detect eight spatiallymultiplexed data streams. We present the implementationarchitecture of this detection algorithm. Using this testbed,future transmission schemes based on 88 spatial multiplexingsystems can be tested and evaluated. Using the testbed, a
measurement was performed in an indoor propagationenvironment. The experimental observations reveal the impact
of spatial correlation on system performance.
-5 0 5 10 15 20 25 3010
-4
10-3
10-2
10-1
100
SNR(dB)
BER
Fig. 6 Performance comparison of measured BER versus measured SNR
REFERENCES
[1]. G. J. Foschini and M. J. Gans, On limits of wireless communications
in a fading environment when using multiple antennas, WirelessPersonal Communications, vol. 6, no. 3, pp.311-335, 1998.[2]. G.G.Raleigh and J.M.Cioffi, "Spatio-Temporal Coding for Wireless
Communication", IEEE Trans. on Comm., vol.44, No.3, pp.257-266,1996.
[3]. P. W. Wolniansky, G. J. Foschini, G. D. Golden, and R. A. Valenzuela,V-BLAST: an architecture for realizing very high data rates overrich-scattering wireless channel, in Proc. URSI InternationalSymposium on Signals, Systems, and Electronics, Sept 1998, pp.295300.
[4]. J. W. Wallace, B. D. Jeffs, and M. A. Jensen, A real-time multipleantenna element testbed for MIMO algorithm development andassessment, in Proceedings of IEEE Antennas and PropagationSociety International Symposium, vol. 2, pp. 17161719, Monterey,Calif, USA, June 2004.
[5]. W. Xiang, D. Waters, T. Pratt, J. Barry and B. Walkenhorst,Implementation and experimental results of a three transmitter
three-receiver OFDM/BLAST testbed, IEEE CommunicationMagazine, vol. 42, pp.88-95, December 2004
[6]. W. Xiang, T. Pratt and X. Wang, A software radio testbed fortwo-transmitter two-receiver space-time coding OFDM wireless LAN,IEEE Communication Magazine., vol.42, pp54-62. June 2004.
[7]. J. Kepler, T. Krauss, and S. Mukthanvaram, Delay SpreadMeasurements on a Wideband MIMO Channel at 3.7GHz, IEEEVTC-2002/Fall, vol. 4 pp. 24982502, 2002.
[8]. H. Bolcskei, D. Gesbert, and A. J. Paulraj, On the Capacity ofOFDMBased Spatial Multiplexing Systems, IEEE Trans. Commun.,vol.50, no.2, pp.225234, 2002.
[9]. P. Murphy, F. Lou, A. Sabharwal, and J. P. Frantz, An FPGA basedrapid prototyping platform for MIMO systems, in Proceedings of 37thAsilomar Conference on Signals, Systems and Computers, vol. 1, pp.900904, Pacific Grove, Calif, USA, November 2003.
[10]. T. Horseman, J.Webber, M. K. Abdul-Aziz, et al., A testbed for
evaluation of innovative turbo MIMO-OFDM architectures, inProceedings of 5th European Personal Mobile 10 EURASIP Journal onApplied Signal Processing Communications Conference (EPMCC 03),pp. 453457, Glasgow, Scotland, UK, April 2003.
[11]. 3G Americas White Paper, UMTS Evolution from 3GPP Release 7 toRelease 8HSPA and SAE/LTE, June 2007.
[12]. 3GPP TR 36.814 v0.1.0, "Technical Specification Group Radio AccessNetwork: Further Advancements for E-UTRA Physical Layer Aspects,"http://www.3gpp.org.
[13]. Y.Lan, Z.Zhang and H. Kayama, "Orthogonal Grouping-based NearOptimal Detection Algorithm for High Order MIMO Systems," toappear in IEEE PIMRC 2009.
[14]. J.Chen, E.Zhou, X.Hou, Z.Zhang, H.Kayama, "A dynamic layerordering M-paths MIMO detection algorithm and its implementation,"APCC 2008. 14th Asia-Pacific Conference on Comm., pp.1-5, 2008.
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