A MULTIPLE ANTENNA SYSTEM FOR ISM-BAND TRANSMISSION

J. Rinas, R. Seeger, L. Brotje, S. Vogeler, T. Haase, K.D. Kammeyer

Department of Communications EngineeringUniversitat Bremen

rinas, seeger, broetje, vogeler, kammeyer@ant.uni-bremen.de

ABSTRACT

In this paper we present a multiple antenna system forindustrial, scientific, and medical (ISM)-band transmission(MASI). The hardware demonstrator was developed and re-alized at our institute. It enables multiple input multipleoutput (MIMO)-communication applications and is capableto transmit arbitrary signals using 8 transmit and 8 receiveantennas in parallel. It operates in the 2.4 GHz ISM-band.The hardware concept is introduced and some design spec-ifications are discussed.

Using this transmission system we present some mea-surement results to show the feasibility of MIMO conceptscurrently under discussion. The applications include trans-mit and receive diversity for single carrier and OFDM aswell as blind source separation (BSS) techniques.

Keywords: Hardware Demonstrator, MIMO, OFDM,Alamouti, Blind Source Separation

1. INTRODUCTION

One impetus to built a MIMO hardware demonstrator is thatthe assumptions made about real channels may be incorrect,and the behavior of MIMO systems should be investigatedunder realistic conditions. Therefore it is sufficient to trans-mit and receive over a real channel and process the receiveddata offline at the workstation environment. This basic idearoots in [Haa99] where a single antenna system was real-ized at the University of Bremen. Furthermore, offline pro-cessing significantly reduces the complexity of the simula-tor. In contrast to a realtime simulator, which is based onsuboptimal frontend processing (due to strict timing con-straints in connection with limited performance of DSP orFPGA chips) [GMea01][BWKD][HWNB03][MLF03], thisconcept has enabled us to freely investigate optimal and sub-optimal algorithm implementations1. On the other hand,we do not claim to substitute a MIMO channel sounder[TRT+00]. A channel sounder is a highly accurate mea-surement system to precisely acquire the (MIMO) channel

1Assuming, that we have an optimal algorithm in idiosyncratic sense,we can neglect implementation issues (quantization errors) on a double-precision machine

parameters. This requires extraordinary effort on e.g. cali-brated and synchronized time bases at the transmitter andreceiver, highly linear frontend amplifiers, and calibratedantenna arrays. In contrast, the objective of our demonstra-tor is to evaluate MIMO algorithms under non-idealized en-vironments deploying common hardware components. More-over, thanks to selectable frontend processing we can han-dle arbitrary radio interface standards, such as single carrier,multicarrier and spread spectrum MIMO systems.

2. HARDWARE CONCEPT

2.1. Top-level System Description

The top-level system is diagrammed in Fig. 1. At the work-station environment in-phase and quadrature (I/Q) data, e.g.Hiperlan/2 or UMTS frames, are generated by the simu-lation system of choice. The impulse shaping is done inthe digital domain. The data is scaled and quantized tomeet the hardware demonstrator concerns and finally storedinto a file. Due to its wide distribution the USB interfaceis chosen to connect the hardware demonstrator with theworkstation. To transfer the I/Q data via the USB inter-face we use a customized application software which al-lows us to set several parameters, like sample rate (fromexternal or internal clock), local oscillator (LO) frequencytuning value and assignment of data files to correspondingantennas. Furthermore, in a Matlab environment we can di-rectly access the demonstrator by calling a Matlab function[KK01]. This is useful for fully automated measurements.Inside the demonstrator the I/Q data is stored into digitalbuffers which are addressed in a circular manner: the incre-ment pointers for memory accesses wrap to the beginningof the buffer when its end is reached. The currently ad-dressed I/Q words are fed to a digital-to-analog converter(DAC), whose analog baseband output signals drive the ra-dio frequency (RF) stage, which performs up-conversion tothe desired RF frequency band.

At the receiver, the RF passband signal is down-con-verted to the complex baseband and undergoes analog-to-digital conversion. A snapshot is stored into a digital buffer.

USBMASI

Digital BufferADCRF RX

USBPC

Matlab etc.(OpenPlattform)

MASI

Digital BufferDACRF TX

PC

Matlab etc.(OpenPlattform)

Off-line Processing Off-line ProcessingReal-time Processing

Fig. 1: Principle block diagram

Fig. 2: The Multiple Antenna Receiver for ISM-BandTransmission. Currently, the receiver and transmitter areequipped with 8 modules

Because frame synchronization is not implemented in hard-ware the receive buffer has doubled length of the transmitbuffer to ensure that at least one complete frame is captured.The sample rate is adjustable up to 80 MHz and may be cho-sen from a set of internally predefined frequencies or an ex-ternal source. The request for extensibility of the hardwaredemonstrator led to a full modular architecture; for each an-tenna the connected transmitter or receiver hardware has itsown plug-in module (see Fig. 2). The digital clock and LOsignal is provided to all modules by a central clock base toensure inter module synchronization of sample rate and car-rier phase.

Low cost software radios are the main driver for mod-ern radio architectures (universal receivers that can accom-modate many different standards). Consequently, this typeof receiver gains increased attention. An all digital receiverperforms all its operations in the digital domain, except thefrontend baseband translation and anti-aliasing filtering. ItsADC sampling clock is not synchronized to the transmittersymbol clock. Therefore, many analog components, suchas the voltage-controlled-oscillator (VCO), are not required.Thus, it can be smaller, more robust, and less expensive.

However, as a fixed sampling clock is used which is not syn-chronized to the transmitter clock, symbol timing and car-rier recovery have to be accomplished in the digital domain.In order to reduce analog component count in the RF stagethe direct conversion (or homodyne) architecture is imple-mented, which performs passband to baseband translationand vice versa directly without intermediate frequency (IF)stages. Traditionally the direct conversion architecturewasconsidered impractical due to severe realization problems.So far, it was hardly possible to fulfill all requirements likeexceptionally linear low noise amplifier (LNA) and mixercircuits, as well as the LO isolation resulting in a lower sen-sitivity compared to heterodyne receivers [Lee98]. How-ever, recent advances in chip technology enabled robust di-rect conversion frontends. In the next section we will dis-cuss the employed components and some important param-eters in a more detailed manner.

2.2. Detailed Description of Components

The direct conversion architecture leads to very simple RFdesigns. Extra intermediate frequency (IF) stages with am-plifiers, passive bandpass filters and oscillators are omitted,as this simplifies the board design and reduces power dis-sipation. Furthermore, due to zero IF the image rejectionproblem does not exist2. All subsequent processing cantake place at the lowest possible frequency which makesthe direct conversion scheme amenable to integrated circuit(IC) implementation. Applying this architecture we are re-stricted to complex baseband processing which halves thesignal bandwidth but doubles the component count in com-parison to a passband scheme.

2.2.1. Low Noise Amplifier (LNA)

The first stage of the receiver is a low-noise amplifier (LNA),whose main function is to provide enough gain to overcome

2In a heterodyne receiver the first IF is normally chosen relatively highto move the image far away from the desired signal in order to relax thefrontend bandpass filter requirements. A direct conversion receiver doesnot need a frontend filter, however it is practically needed to avoid out-of-band interferes overloading the frontend [Lee98]

the noise of subsequent stages (such as the mixer). Asidefrom this providing gain while adding as little noise as pos-sible, an LNA should accommodate large signals withoutdistortion. It must also present an impedance of 50Ω tothe input source since the transfer function of the preced-ing filter is quite sensitive to the quality of termination. Theemployed LNA chip has a gain of 22 dB and a noise figure(NF) of 1.6 dB at 2.4 GHz. A relatively high 1 dB compres-sion point (the input power at which the gain is 1 dB lessthen expected) of 4.2 dBm and a high third order interceptpoint (IP3) ensures wide range linear operation.

2.2.2. Mixer

Since for direct conversion architectures the LO frequencylies in the desired frequency band, the LO signal, which nor-mally has much more power than the received signal, canleak into the RF input of the mixer or possibly finds its wayto the antenna. The self-mixed LO signal results in a time-invariant DC baseband component, which can drive subse-quent stages into saturation. In addition, any even-order dis-tortion produces a DC offset that is signal-dependent, so thesecond-order intercept point (IP2) is a very important pa-rameter for direct conversion schemes. The employed ICquadrature demodulator has two integrated Gilbert (or four-quadrant) cell mixers. This mixer style provides reasonableconversion gain (IF power output with respect to the RFpower input), as well as good rejection at the RF and LOinput ports and the IF output due to the complete differen-tial design.

External amplifiers are omitted due to integrated RF andbaseband AGC amplifiers, which provide about 70 dB gaincontrol. A high dynamic range is indispensable for wire-less application. The baseband I/Q output ports allow directconnection to the ADCs.

2.2.3. Analog-to-Digital Conversion

The analog-to-digital converter (ADC) converts the contin-uous-time stimuli signals to discrete-time binary-code form.For communications applications the dynamic measures ofan ADC, such as signal-to-noise ratio (SNR), spurious-freedynamic range (SFDR) and two-tone intermodulation dis-tortion (IMD) are figures of merit [Wal99]. The effectivenumber of bits (accuracy) depends strongly on these dy-namic measurements. High speed ADCs are extremely sen-sitive to the quality of the sampling clock. The internal trackand hold circuit is essentially a mixer. Any noise, distortion,or timing jitter on the clock signal will be combined withthe desired signal at the ADC output in addition to internaltiming error sources (aperture jitter). A phase-locked loop(PLL) based synthesizer normally exhibits a higher phasenoise value than a fixed frequency clock generator. How-ever, to provide several customized sample rates a set of

stable crystal-controlled oscillator circuits is used. Further-more, an external clock input up to 80 MHz is available.The chosen 12bit ADC chip delivers good dynamic mea-surements, a low aperture jitter and was available at smallquantities. The digital outputs (I and Q branches) are di-rectly connected to the digital buffer circuit.

2.2.4. Digital Buffer

The digital buffer stores the raw data, delivered by the ADC(receiver) or provided by the USB-controller (transmitter).At the transmitter the digital buffer serves as a circular buffer.Once the data is completely stored, the buffer is linearly ad-dressed, when the last address is reached the address counterwraps around to the first address and counts up again. Where-as at the receiver only one frame is captured when the trig-ger event occurs. Because large FIFO chips are very ex-pensive and hardly obtainable at small quantities, the dig-ital buffer circuit is realized by a field programmable gatearray (FPGA) and static RAM (SRAM). In contrast to dy-namic RAM, SRAM does not need refresh cycles and offersa considerably simpler interface. The employed zero busturnaround (ZBT) RAMs are fast synchronous SRAM chipswhich are directly connected to the FPGA. Providing inter-leaved read/write without wasteful turnaround cycles, theZBT RAM is predestined for capturing applications. Onceprimed with an address, it can read/write one word of dataper clock cycle. Up to220 samples can be captured per in-phase and quadrature branch. The FPGA connects all digitalbusses and provides several control signals. Due to the abil-ity of reconfiguration, it offers a high degree of flexibility. Italso provides enough resources to hold optional customizedfrontend processing logic, like frame detection algorithms3.The logic blocks are described at a high abstraction levelusing VHDL4.

3. MEASUREMENTS AND APPLICATIONS

The measurements were performed in an indoor environ-ment, i.e. we transmitted between two adjacent office roomsof approx. 20 sqm size each. The total transmit power was17 dBm (50 mW).

3.1. Frame Synchronization

Our system works without any wired connection betweenthe transmitting and receiving end. Therefore we have to

3The physical memory (ZBT RAM) has identical size, but the addresslogic of the circular buffer is programmed according to user settings. No-tional, frame synchronization could be implemented in hardware. Thus,the full physical buffer size could be used at transmitter, however, with thedrawback of a fixed preamble or frame structure.

4Very High Speed Integrated Circuit (VHSIC) Hardware DescriptionLanguage (VHDL)

AD8347Direct-Down-Conversion

XilinxXCV50EFPGA

2.4 GHz

RFin

LOin

~16 MHz

16 MHz

2x AD9432ADC12bit80MSPS

ZBT-RAM1M x 24bit

I

Q

Power5VRF

RF on/offPower1.8Vdigital

Power3.3Vdigital

RF Unit

Bac

kpla

ne

B

us

Syst

em

Power5Vanalog

~

LNA

Fig. 3: Receiver module

AD8346Direct-Up-Conversion

XilinxXCV50EFPGA

2.4 GHzPA

RFout

LOin

~16 MHz

16 MHz

AD9765DAC2x12bit125MSPS

ZBT-RAM1M x 24bit

I

Q

Power5VRF

RF on/offPower1.8Vdigital

Power3.3Vdigital

RF Unit

Bac

kpla

ne

B

us

Syst

em

Power5Vanalog

~

Fig. 4: Transmitter module

synchronize both sides. We transmit periodically repeatedframes withLt samples. In order to get at least one com-plete frame we sampleLr = 2 · Lt values at the receivingside.

The first task is the detection of the starting point of onecomplete frame within thisLr samples. Therefore we applya simple power detection scheme, which presents a prag-matic approach for our measurement system, because it ismostly independent from impairments like frequency offsetand frequency selective channels, and can be used with anymodulation scheme. For the power detection we normallyconsider aboutLZ = 1000 samples within one frame oflengthLt. The high variation of the envelope of the signalis unproblematic since we are using a very slow AGC.

Our synchronization approach is a sliding power detec-tion. We detect the current power of the received signalr(k)(one channel) by averaging overLZ successive samples ofboth gaps (Fig. 5).

kstart = arg mink

p (k)

= arg mink

k+LZ−1∑

κ=k

12LZ

(

|r (κ)|2

+ |r (κ + Lt)|2)

with k = 0 . . . Lt − LZ

(1)

−1

0

1foff

=762.9 Hz

r 0(k)

−1

0

1foff

=762.9 Hz

r 1(k)

−1

0

1foff

=762.9 Hz

r 2(k)

2 4 6 8 10 12

x 104

−1

0

1foff

=762.9 Hz

r 3(k)

samples

Fig. 5: Measured signals with frame synchronization

This approach for a coarse frame synchronization is notnecessarily limited to MIMO setups but can also be usedfor single input single output (SISO) channels. An examplefor this scheme is presented in Fig. 5, where you can seetime series of a measurement including the detection of thecomplete frame.

3.2. Frequency Responses

In this section we will present a setup for measuring the fre-quency response of the MIMO transmission channel, whichwe always consider from the digital domain at the transmit-ter to the digital domain at the receiver - including all effectsof the system components. We have to emphasize that it isnot our intention to do systematic channel measurements.

For measurements we apply a chirp-like signal, whereasonly one transmitter is sending at a time, in order to measurethe complete matrix of frequency responses (Fig. 6).

r

H

chirp-like signal

transmitter receiver

0 20 40 60

real

m(k

)

Fig. 6: Multiplexing for channel measurement

This signal is designed in the frequency domain as

M(n) = e−j π

NDFTn2

for n = 0 . . . NDFT − 1 , (2)

because this guarantees an exactly flat magnitude. Process-ing the IDFT, we get the time domain signal

m(k) = IDFTNDFTM(n) (3)

which is inherently periodic. We exploit this property andsendm(k) in a periodic way so that only a coarse synchro-nization is necessary.

The quadratic phase increment leads to a small crest fac-tor5of the signal. In our case withNFFT = 128 the crestfactor for the imaginary part of the signalm(k) is

cimag =max imag m(k)

√

1NDFT

NDFT−1∑

k=0

imag m(k)2

≈ 1.47 . (4)

We can measure the frequency response, up to a linearphase uncertainty, by using a fractional part of the receivedtime signal withNDFT samples and calculating

R(n) = DFTNDFTr(koffset + k)

k = 0 . . . NDFT − 1(5)

H(n) =R(n)

M(n). (6)

Fig. 5 shows the time series of one measurement. No-tice the different amplitudes of the signal that correspondtoone constellation of the multiplexing scheme (Fig. 6). Sincethis method is sensitive regarding the frequency offset, weadded a pilot sequence to our measurement frame in orderto estimate and correct the offset.

The advantage of this approach is that we only need acoarse synchronization and not a high-precision time refer-ence (like in channel sounding setups). Therefore the start-ing positionkoffset may be slightly inaccurate. This circu-lar6 time shift of the starting position will result in a linearphase term, but it does not influence the shape of the mag-nitude response:

Hshift(n) =DFTNDFT

r(k+kshift)

M(n)

= H(n)ej 2π

NDFTnkshift .

(7)

Fig. 7 depicts 3 different frequency response measure-ments using 4 transmit and 4 receive antennas. Uniformlinear arrays (ULAs) withλ/2 spaced elements are used.The sampling frequency was set tofs = 50 MHz.One directly can see the filter influence of our transmis-sions system, which limit the signal to the3 dB range of

5The peak-to-rms voltage ratio of an alternating current (AC)signal6Since we are using a periodic repeated signal, we can interpret a time

shift as a periodic time shift.

−20 0 20

−40

−20

0

mag

. in

dB

−20 0 20

−40

−20

0

−20 0 20

−40

−20

0

−20 0 20

−40

−20

0

−20 0 20

−40

−20

0

mag

. in

dB

−20 0 20

−40

−20

0

−20 0 20

−40

−20

0

−20 0 20

−40

−20

0

−20 0 20

−40

−20

0

mag

. in

dB

−20 0 20

−40

−20

0

−20 0 20

−40

−20

0

−20 0 20

−40

−20

0

−20 0 20

−40

−20

0

mag

. in

dB

f in MHz−20 0 20

−40

−20

0

f in MHz−20 0 20

−40

−20

0

f in MHz−20 0 20

−40

−20

0

f in MHz

Fig. 7: Frequency responses for a 4x4 setup

approx. ±16 MHz. In addition there are some notches inthe spectrum that arise from a frequency selective channel.Our measurements revealed, that already a small change ofthe position may have a strong impact on the frequency re-sponse.

3.3. Diversity techniques

There are two principle approaches to get a performancegain from an antenna array. One approach uses the knowngeometric constellation of the antennas for beamforming.The other approach is independent of the array constellationand increases the diversity of the system.

In this section we focus on diversity techniques. Di-versity through a multi antenna setup can be attained at thereceiving as well as at the transmitting end.

3.3.1. Receive diversity

In order to achieve a diversity gain at the receiving side wecan expand a SISO setup and use multiple receive anten-nas. The diversity combining can be done in a blind way byusing the spatial covariance of the received signal streams.Timing offset is estimated after combining using the ap-proach presented in [Lee02].

For combining we have to take into account that our sys-tem has independent AGCs in each channel. Therefore wehave to estimate the noise level, which is done by exploitingthe power gap in the frame of the received signal.

In order to show the gain of a combining we sent oneQPSK signal and received it with multiple antennas. Fig. 8depicts the signal constellations of a measurement. On theleft hand side you can see the signal constellations receivedfrom each antenna, while the right hand side depicts the

−1

0

1

−1

0

1

−1

0

1

−1 0 1−1

0

1

17.1 dB

8.4 dB

18.8 dB

9.4 dB

r 1r 2

r 3r 4

−1

0

1

−1

0

1

17.4 dB

−1

0

1

−1 0 1−1

0

1

17.1 dB

19.3 dB

22.3 dB

r 1r 1

2r 1

23

r 1234

Fig. 8: Combining gain with estimated SNR

combining of the signals received by antenna one up to four.The rising SNRs for increasing number of signals involvedin the combining process indicate the combining gain.

SNR estimation is done using the approach presentedin [Mat93], because it doesn’t suffer from wrong symboldecisions and is suitable without modification for all PSKschemes. The SNR of a single data streamy is calculatedby

p =

√√√√√√

2 −E

|y|4

E

|y|22 (8)

SNR =p

1 − p. (9)

3.3.2. Transmit Diversity

In theory, receive diversity and transmit diversity are inter-changeable. In the following we will discuss transmit di-versity schemes, especially the so called orthogonal space-time block codes (OSTBC), under more realistic conditions.Channel estimation and carrier offset estimation are essen-tial tasks in coherent receivers. However, they are also somekind of error sources due to the imperfectness of the em-ployed algorithm.

Alamouti [Ala98] discovered a remarkable transmit di-versity scheme for transmission with two antennas. Thisscheme supports maximum-likelihood detection based onlyon linear processing at the receiver and is able to achievefull diversity provided by the number of transmit and re-ceive antennas. The input symbols to the ST block encoderare divided into groups of two symbols eachs1, s2. At agiven symbol period,s1 and−s∗2 are transmitted from an-tenna1 and2, respectively, and at the consecutive symbolperiod,s2 ands∗1 are transmitted from antenna1 and2, re-spectively. Leth1 andh2 be the channel coefficients fromthe first and second transmit antennas, respectively. It isassumed thath1 andh2 are constant over two consecutivesymbol periods. Consider a receiver with one receiver an-tenna and denote the received signals over two consecutivesymbol periods asr1 andr2. Defining the code symbol vec-tor s = [s1s

∗2]

T and the received vectorr = [r1r∗2 ]T , we

getr = Hs + η (10)

where the channel matrix

H =

[h1 −h2

h∗2 h∗

1

]

, (11)

and the noise vectorη = [η1η2]T are used. The AWGN

is represented byη1 andη2 which are modelled as i.i.d com-plex Gaussian random variables with zero mean and powerspectral densityN0/2 per dimension. Henceη is a Gaus-sian random vector with zero mean and covarianceN0I.

The decoding procedure consists of a simple multiplica-tion with the hermitian channel matrixHH , hence

r = HHHs + H

Hη (12)

whereH is theestimatedchannel matrix. Considering im-perfect channel estimation with an estimation error [SBF01]

∆h = ∆hnoise+ ∆hDoppler (13)

it follows

H =

[h1 + ∆h1 −(h2 + ∆h2)h∗

2 + ∆h∗2 h∗

1 + ∆h∗1

]

. (14)

The (soft) decoded symbol-vectorr = [r1r∗2 ]T can be

obtained using (12) and (14)

r =

[|h1|

2 + |h2|2 0

0 |h1|2 + |h2|

2

]

︸ ︷︷ ︸

desired

s (15)

+

[h1∆h∗

1 + h∗2∆h2 −h2∆h∗

1 + h∗1∆h2

−h1∆h∗2 + h∗

2∆h1 h2∆h∗2 + h∗

1∆h1

]

︸ ︷︷ ︸

influence of estimation errors

s

+ HH

η︸ ︷︷ ︸

noise

From (15) it is clear that channel estimation errors lead tospatial intersymbol interference (ISI) if the estimated chan-nel matrixH is not unitary. (14).

Another major task for coherent receivers is the carrierfrequency offset estimation and correction. Consider twoconsecutive received symbolsr1 andr2. The frequency off-set can be modelled by the time-domain multiplication withthe two phasorsejϕ1 andejϕ2 , respectively. Using the sys-tem model (10) it can be stated

[ejϕ1 00 e−jϕ2

] [r1

r∗2

]

=

[ejϕ1 00 e−jϕ2

]

(Hs + η) . (16)

Assuming perfect channel estimation conditions, i. e.H =H, and neglecting the noise term in (16), we obtain

[h∗

1 h2

−h∗2 h1

] [ejϕ1 00 e−jϕ2

] [h1 −h2

h∗2 h∗

1

]

s

=

[|h1|

2ejϕ1 + |h2|2e−jϕ2 −h∗

1h2ejϕ1 + h2h

∗1e

−jϕ2

−h∗2h1e

jϕ1 + h1h∗2e

−jϕ2 |h2|2ejϕ1 + |h1|

2e−jϕ2

]

s.

(17)In contrast to a single transmit antenna system, the loss oforthogonality due to a (residual) frequency offset leads tomagnitudevariations. A comparison of a simulated and ameasured signal constellation with channel estimation andfrequency offset is depicted by Fig. 9.

−2 0 2−2

−1

0

1

2a)

−2 0 2−2

−1

0

1

2b)

Fig. 9: QPSK signal constellations at the STBC decoderoutput: a) simulated signal, b) measured signal

3.4. OFDM Transmission

Our simulation tool for OFDM transmission is based on theIEEE 802.11a WLAN standard [IEE99], except for the car-rier frequency of 2.4 GHz (instead of 5.2 GHz).

3.4.1. Synchronization

Timing Synchronization- First of all, a coarse frame syn-chronization according to the method for single carrier sys-tems already described (3.1) is carried out. For OFDMtransmission, there is no need finding the starting point of

the burst exactly, because afterwards the position of the FFTwindow is adjusted in a second synchronization step.

Due to the cyclic prefix in every OFDM symbol, the ex-act position of the FFT window can be found by correlationover the received signal. This results in a well defined max-imum value for each OFDM symbol, the correct FFT win-dow start position isNguardsamples later. Averaging OFDMsymbols to suppress noise may be reasonable.

Carrier Frequency Synchronization- The correction ofcarrier frequency offsets (CFO) in OFDM systems can becarried out in two steps. A synchronization in time do-main (before processing the FFT at the receiver) is abso-lutely necessary, because severe intersymbol interference(ISI) occurs in frequency domain if a CFO is not correctedbefore. In case of small frequency offsets (compared withthe subcarrier spacing) the main effect after processing theFFT is a rotation of symbols regarding their signal constel-lation, so that in time domain a coarse synchronization issufficient. This coarse estimation is accomplished by cal-culating the phase deviation between the two preamble Csymbols [IEE99].

A fine carrier frequency synchronization in frequencydomain is based on the 4 pilot symbols which are includedin every OFDM symbol and whose carrier positions are sym-metric to the carrier with frequencyf = 0. The pilot car-riers are BPSK modulated. To estimate the CFO from thepilot symbols, the channel coefficients according to thesecarriers have to be known. Because every OFDM symbolcarries the pilot information, a tracking of the CFO esti-mation can easily be performed. For further details on oursynchronization methods see [Sch01].

3.4.2. Impact of a DC Offset

A DC offset, e.g. resulting from self mixing of the oscil-lators as described in 2.2, is a serious problem when us-ing direct conversion concepts. With regard to a transmis-sion, we have to take into account different aspects. On theone hand, the coarse burst synchronization fails with signalshaving high DC offsets. Therefore, it is necessary to aver-age the whole received sequence to get an estimate for theDC offset and to subtract it afterwards. On the other hand,assuming a correct synchronization, the impact of the DCoffset at the receiver in frequency domain is, due to the rect-angular windowing of the FFT, the same as an addition of asinc function with the maximum atf = 0 and zero cross-ing at all other subcarrier frequencies. That is why the DCsubcarrier is unassigned in IEEE 802.11a. In fact, this is nosolution, because in combination with a carrier frequencyoffset the DC offset affectsall subcarriers. In this case, thesinc function’s maximum is shifted by the value of the CFOand additionally the zero crossings movebetweenthe sam-pling points of the subcarriers.

Fig. 10 (left hand side) shows the signal constellation di-agram of a 54 Mbit/s data burst transmitted with our systemat 2.4 GHz after equalization. The received signal containsa DC offset of approx. 7 dB (ratio of DC magnitude to rms7

of signal without DC) and a CFO of approx. 104 kHz.

−2 −1 0 1 2−2

−1

0

1

2

−2 −1 0 1 2

Fig. 10: Impact of a DC offset (7 dB): signal constellationdiagram (uncorrected/corrected)

When correcting the DC offset, an estimation based onaveraging a certain number of preamble B symbols in timeleads to sufficient results (see Fig. 10, right hand side).

3.4.3. Transmit Diversity Schemes for OFDM

Considering transmit diversity schemes for IEEE 802.11a,one can distinguish between schemes which are compatibleto the standard and those which are not. Space time blockcodes (STBC) belong to the latter ones. In contrast, delaydiversity schemes need no modification of the receiver atall, thus they are fully standard conform.

Delay Diversity and Cyclic Delay Diversity- Delay di-versity means transmitting the same OFDM symbol in timedomain, including the cyclic prefix, with a certain delay foreach antenna. Due to synchronization constraints, the maxi-mum delay is restricted to the remaining length of the cyclicprefix, which is the total length of the cyclic prefix minusthe channel impulse response length. A better solution es-pecially for OFDM systems iscyclicdelay diversity (CDD),e. g. known from [DK02] and [BC02]. Using cyclic delaydiversity, cyclically time-shifted OFDM symbols aresimul-taneouslytransmitted by each antenna. It is important tonote, that the signal is shiftedbeforeinserting the cyclic pre-fix (CP). Compared to noncyclic delay diversity there is nostrong restriction for the length of the delay. The allowedmaximum length equals to the FFT length.

The principle of all delay diversity schemes is to in-crease the length of the channel impulse response seen at thereceiver, i. e. the channel transfer function becomes morefrequency selective. The added diversity is only exploited

7root mean square:

√

1/N∑

N

k=1|s(k)|2

by the channel decoder [BC02], in contrast to other trans-mit diversity schemes there is no SNR enhancement. Be-cause the superposition of the transmitted signals from eachantenna at the receiver using delay diversity or cyclic delaydiversity is equivalent to a single transmit antenna systemwith extended channel impulse length, no changes are nec-essary at the receiver.

Fig. 11 shows the increased frequency selectivity due toCDD by means of two measured channel transfer functionsat 2.4 GHz. To the right, a CDD system with 2 transmit an-tennas and a delay of 6 samples (≡ 0.3µs) was used. Forcomparison the single antenna case is presented in the leftfigure. The magnitudes of the channel coefficients for eachsubcarrier in the OFDM system were obtained by the esti-mation based on the IEEE 802.11a preamble.

Although there is no restriction for the delay length us-ing CDD in theory, problems may occur if a noise reduction(NR) of the estimated channel transfer function by window-ing in time domain [Sch01] is carried out. In this case, theincreasedchannel impulse length due to CDD has to be con-sidered when fixing the NR window length. If the windowlength is too short, the channel impulse response will befalsified which significantly reduces the performance of theNR.

1 10 20 30 40 50−20

−15

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0

5without CDD

subcarrier index

|Cn| i

n dB

1 10 20 30 40 50−20

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subcarrier index

Fig. 11: Measured channel transfer functions

STBC: Alamouti Scheme- The transmit diversity schemeproposed by Alamouti [Ala98] is based on non frequencyselective or flat fading channel assumptions. Therefore, incase of OFDM the coding of the transmit symbols accordingto Alamouti has to be done infrequencydomain, i.e. beforeprocessing the IFFT at the transmitter, and decoding afterapplying the FFT at the receiver. So in contrast to delaydiversity schemes, two IFFT processing units are needed.Because the OFDM demodulation (FFT) is the inverse oper-

ation of the modulation (IFFT), the equations describing theAlamouti coding (10) remain unchanged for OFDM trans-mission, except for the transmit symbolssi as well as thereceive symbolsri becoming OFDM symbols in frequencydomain, i.e. they consist of 52 (number of subcarriers) PSKor QAM symbols each.

In contrast to all delay diversity schemes, using the Alam-outi transmit diversity scheme isnot compatible to IEEE802.11a. In addition to the modifications in the receiveraccording to the Alamouti decoding, a modification of thechannel estimation and synchronization algorithms as wellas a new preamble structure is necessary.

3.4.4. Receive Diversity Scheme for OFDM

The application of receive diversity to a transmission systembased on IEEE 802.11a can be realized without any changesto the transmitter, i.e. absolutely standard conform. Onepossible method is maximum ratio combining (MRC), onwhich we will focus in the following.

Maximum Ratio Combining- In order to combine thereceived symbols according to the MRC principle, in fre-quency domain, the not yet equalized values on each subcar-rier are multiplied with the corresponding conjugate com-plex channel coefficient. The resulting values of all receiveantennas are then added up separately for each subcarrierand afterwards, in case of QAM symbols, normalized tothe sum of power of the channel coefficients resulting fromthe different antennas. After a soft-decision demapping, theweighted bits are multiplied with the inverse of the normal-ization factors used before.

A measurement example obtained with MASI can beseen in Fig. 12. In that case, the BER for the signal receivedon the first and second receive antenna after channel decod-ing is 0.11 and0.5, respectively. MRC of the two receivedsignals (s. Fig. 12c) results in a reduction of the BER to7.97 · 10−3, whereas the combining of four receive signals(s. Fig. 12d), obtained with two additional receive antennas,leads to an error-free reception.

3.5. Blind Source Separation

Blind source separation (BSS) algorithms are able to sep-arate different signals from a multi sensor setup. The onlyknowledge used to achieve this goal is that the signals shouldbe statistically independent.

To apply source separation techniques in communica-tions we are using the setup depicted in Fig. 13. First of allthe DC-offset caused by the direct conversion frontend isremoved. After root raised cosine filtering a frame synchro-nization according to section 3.1 is carried out. To separatethe independent components we can apply a BSS algorithmdirectly to the oversampled signal. For this step we choose

−1 0 1

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a)

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b)

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Fig. 12: Signal constellations: a) receive signal 1, b) receivesignal 2, c) MRC of receive signals 1 and 2, d) MRC ofreceive signals 1 to 4

the JADE [CS93] algorithm8 as a spatial only separation ap-proach.

The separation leads to data streams which are processedin the classical way like in single antenna systems. We syn-chronize to the symbol timing using the method presented in[Lee02]. In order to determine the carrier frequency offsetwe apply a non-linearity and a frequency estimation.

Measurements were done with a sampling frequency offs = 10 MHz in order to get an approximately flat channel.

8We also successfully used other approaches like fastICA [BH00] andSSARS [FK98].

removeDC-Offset

receive-filter

frame-splitting

frame-detection

Blind SourceSeparation

timingestimation

frequency andphaseestimation

downsampling exp ( )

processing of other layers

Fig. 13: BSS setup

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Fig. 14: 2x2 signal constellations before and after BSS

In order to visualize the successful separation we simultane-ously transmit signals with different modulation schemes.

Fig. 14 depicts the separation of a BPSK and a QPSKsignal sent in parallel and received by two antennas. Thesignal constellation before separation is obtained by usingthe timing information estimated after separation. As onecan see in Fig. 14 the signal streams are properly separated.The graphs on the left show that in this particular measure-ment the signal of the BPSK signal was dominant.

The separation procedure can be easily extended to asystem with four transmit and receive antennas. The resultsare depicted in Fig. 15. It can be seen that even in this situ-ation a proper blind separation is possible.

Based on our experiences we can state that it is prac-tically possible to apply separation algorithms for separa-tion of communication signals in MIMO setups, even if theproperties of the modulation schemes are not taken into ac-count. This makes our setup interesting for interference sce-narios. If a knowledge of the symbol alphabet of a signal isadditionally exploited, the BSS can be used as a frontend tospatial interference cancellation algorithms like VBLAST[WFGV98].

4. CONCLUSIONS

In this paper we introduced a very flexible low cost mea-surement system which allows the testing of nearly allMIMO communications setups currently under discussion.Arbitrary signals can be generated and transmitted in realtime. However, the offline processing concept significantlyreduces the complexity of the demonstrator. In contrast to

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Fig. 15: 4x4 signal constellations before and after BSS

a realtime simulator, this has enabled us to freely investi-gate optimal and suboptimal algorithms. Moreover, we arenot limited to a special simulation software. A wide rangeof applications was presented. In order to show the natureof the MIMO channel we accomplished some indoor mea-surements of frequency responses. Furthermore, receiveand transmit diversity schemes to gain performance fromthe spatial channel were considered. In theory, receive andtransmit diversity are interchangeable. However, in practicewe observed that orthogonal STBCs are more sensitive toestimation errors. As an example for OFDM, we evaluated asystem according to IEEE 802.11a to which we successfullyapplied several transmit and receive diversity schemes. Thefeasibility of BSS for communications systems under real-istic conditions was studied. During our indoor measure-ments we could hardly produce scenarios that prevent theBSS from working. Consequently, BSS algorithms, whichcan be directly applied to the oversampled received signalwithout timing and carrier offset synchronization, are suit-able for robust frontend processing.

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