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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

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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

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