MIMO MEASUREMENTS OF COMMUNICATION SIGNALSAND

APPLICATION OF BLIND SOURCE SEPARATION

J. Rinas and K.D. Kammeyer

Department of Communications EngineeringUniversitat Bremen

Email: {rinas, kammeyer}@ant.uni-bremen.de

ABSTRACT

In this paper we present a flexible multiple input multi-ple output (MIMO) measurement system for communica-tion signals in the 2.4 GHz band. We present some measure-ments of the digital to digital transmission channel whichincludes all impairments of the hardware realization.

Using this system we perform a multi-layer transmis-sion of communication signals. At the receiver side we useblind source separation (BSS) techniques as frontend pro-cessing to avoid estimation and synchronization problems.In order to improve the estimation of the channel and thesymbol detection, we propose an iterative approach, whichuses the knowledge of the finite symbol alphabet but doesnot need additional training data.

1. INTRODUCTION

Multiple input multiple output (MIMO) systems are cur-rently under discussion for communication systems becauseof their ability to exploit the capacity of a spatial channel.Recently, powerful detection algorithms have been devel-oped. These algorithms need a suitable estimation of thetransmission channel. In most cases the channel estima-tion is obtained by including a pilot sequence in the datastream. However, this will lead to a loss of spectral effi-ciency. Therefore we will present a new scheme that com-bines blind source separation techniques with an efficientdetection algorithm in an iterative way. This will lead to anoverall blind detection of the transmitted symbols.

The remainder of the paper is organized as follows: Inthe current section we will introduce briefly our measure-ment system for flexible handling of MIMO transmissions.In section 2 we will present some measurements on the fre-quency response of the MIMO channel under consideration.Section 3 introduces a setup for using BSS techniques in

This work was supported by the German national science foundation(DFG) within the project AKOM (project #Ka 841/9-1)

a communication environment. Some measurements illus-trate the feasibility of this approach. In section 4 we intro-duce an iteration scheme in order to utilize the finite symbolalphabet. The measurements include an estimation of thesignal noise ratio (SNR) to illustrate the advantages of suchan approach. A summary and concluding remarks can befound in section 5.

1.1. Motivation and System Description

When simulating transmission systems many estimation prob-lems are normally ignored or values are taken as ideallyknow. Several assumption have to be made in order to modelthe transmission channel. Finally a simulation only approachmay lead to results that cannot be reproduced in a hard-ware implementation. Therefore we build a flexible hard-ware demonstrator for MIMO communication setups. Thissimulator introduces different impairments to the signal anddemands the inclusion of many estimation tasks.

Our general concept is to have a real world transmis-sion, but an offline processing of the signal in order to in-vestigate algorithms that cannot be implemented in realtimetoday but in the near future. This offline processing ap-proach gives us the flexibility to investigate most MIMOsetups currently under discussion. Our 2.4 GHz demonstra-tor is based on a direct conversion transmitter and receiverconcept. The transmitter and the receiver as well consit of8 modules that are synchronized according to their local os-cillators (LO) and digital control. The transmitter sends anarbitrary periodically repeated signal from its memory (upto 8 × 512k samples). The receiver takes a snapshot ofthe transmitted signal with twice the period length of thetransmitted signal. Sampling frequencies up to 50 MHz arepossible. The received signal is transfered to a PC using anUSB interface and can be processed by a simulation tool ofchoice.

2. FREQUENCY RESPONSES

In this section we will present a setup for measuring the fre-quency response of the MIMO transmission channel. Weconsider the channel from the digital domain at the trans-mitter to the digital domain at the receiver, thus includingall effects of the system components. We have to emphasizethat it is not our intention to do systematic channel measure-ments.

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 (figure 1).

r

H

chirp-like signal

transmitter receiver

0 20 40 60

real

{m(k

)}

Fig. 1. Multiplexing for channel measurement

This signal is designed in the frequency domain as

M(n) = e−j π

NDFTn2

for n = 0 . . . NDFT − 1 , (1)

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

m(k) = IDFTNDFT{M(n)} (2)

which is inherently periodic. We exploit this property andsend m(k) in a periodic way so that only a coarse synchro-nization is necessary.The quadratic phase increment leads to a small crest fac-tor1of the signal.

We can measure the frequency response, up to a linearphase uncertainty, by using a fractional part of the receivedtime signal r(k) with NDFT samples and calculating

R(n) = DFTNDFT{r(k + koffset)}

k = 0 . . . NDFT − 1(3)

H(n) =R(n)

M(n). (4)

Since this method is sensitive regarding the frequencyoffset, we added a pilot sequence to our measurement framein order to estimate and correct the offset.

1The peak-to-rms voltage ratio of an alternating current (ac) signal

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 starting position koffset may be slightlyinaccurate by kshift. This circular2 time shift of the startingposition will result in a linear phase term, but it does notinfluence the shape of the magnitude response:

Hshift(n) =DFTNDFT

{r(k+koffset+kshift)}

M(n)

= H(n)ej 2π

NDFTnkshift .

(5)

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Fig. 2. Frequency responses for a 4x4 setup; a column rep-resents the responses excited by one transmitter

Figure 2 depicts 3 different frequency response mea-surements using 4 transmit and 4 receive antennas. Themeasurements were taken from one office room to an adja-cent office. Uniform linear arrays (ULAs) with λ/2 spacedelements are used. The sampling frequency was set to fs =50 MHz.The filter influence of our transmissions system, which limitthe signal to the 3 dB range of approx. ±16 MHz, canbe seen directly. In addition there are some deep fades 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. BLIND SOURCE SEPARATION

Blind source separation (BSS) algorithms are able to sep-arate different signals from a multi sensor setup. The only

2Since we are using a periodic repeated signal, we can interpret a timeshift as a periodic time shift.

knowledge used to achieve this goal is that the signals shouldbe statistically independent.

interface to iterations

same processing of other layers

exp ( )

estimationphase

downsampling exp ( )

exp ( )downsampling

frame−splitting Separation

Blind Source−

detectionframe−

estimationtiming

estimationfrequency

DC−Offsetremove receive

filter

PSfrag replacements

x0

x1

xnR−1

s0

s1

snT−1

e0

e1

enT−1

Fig. 3. receiver setup for BSS

To apply source separation techniques in communica-tions we are using the setup depicted in figure 3. First ofall the DC-offset caused by the direct conversion frontendis removed. After root raised cosine filtering a frame syn-chronization based on a power detection is carried out. Toseparate the independent components we can apply a BSSalgorithm directly to to the oversampled signal. For this stepwe choose the JADE [1] algorithm as a spatial only separa-tion approach.The separation leads to data streams which are processed inthe classical way like in single antenna systems. We syn-chronize to the symbol timing using the method presentedin [2]. In order to determine the carrier frequency offset weapply a non-linearity and a frequency estimation.

Measurements were done with a sampling frequency offs = 50 MHz. The distance between transmitter and re-ceiver was spanning two office rooms. With an oversam-pling of w = 8 we can consider the transmission channelas nearly flat. In order to visualize the successful separationwe simultaneously transmit signals with different modula-tion schemes.

Figure 4 depicts the separation of a BPSK, QPSK, 8PSKand a 16QAM signal sent in parallel and received by fourantennas. The signal constellation before separation is ob-tained by using the timing information estimated after sep-aration. As one can see in figure 4 the signal streams areproperly separated.

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.

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

4. BLIND SOURCE SEPARATION WITHITERATIVE IMPROVEMENT

The general form of BSS approaches do not take into ac-count the finite alphabet of the modulation signals. Thereforwe present a combination of the BSS and a powerful detec-tion scheme. As a MIMO detections scheme we choose theVBLAST [3] system.

Figure 5 depicts the idea of the combination. The sym-bols s0 . . . snT−1 detected using the blind source separation(figure 3) are used as reference symbols in order to esti-mate the spatial channel matrix H

(i). The estimation is per-formed by taking the pseudo inverse of the previously de-tected symbols. Then new data symbols s

(i)0 . . . s

(i)nT−1 will

be detected using the VBLAST algorithm with this channelmatrix. This new symbols can be used as reference symbolsonce again for a new channel estimation. These iterationswill improve the detection, because the blind source sepa-ration can only design a linear filter but the VBLAST algo-rithm will introduce a successive interference cancellation

estimationchannel

iterationinitialization

VBLAST

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x0

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s0s1

snT−1

H(i)

s(i)0

s(i)1

s(i)nT−1

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e(i)nT−1

Fig. 5. iterative improvement of channel estimation and de-tection

that utilizes the knowledge of the finite alphabet.

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Fig. 6. signal constellations before, after blind source sepa-ration and after 10 iterations – estimated SNR

In order to show the feasibility of this approach fourQPSK signals are transmitted in parallel. On the receiverside the schemes presented in figure 3 and 5 were used.Figure 6 depicts the signal constellation before the sepa-ration, after the separation and after some iterations of thethe scheme presented in figure 5. We have included esti-mations of the SNR of the symbol constellation before thedecision devices [4]. In figure 5 a signal constellation af-ter the BSS consists nearly of noise. Using the proposediteration scheme even this constellation can be resolved to

QPSK. The reason for this is the ranking of the successiveinterference cancellation in the VBLAST detection. Thisranking is based on the estimated channel coefficients thatare refined iteratively.It can be stated that by utilizing the symbol alphabet in aniterative way the SNR can be significantly improved.3

5. CONCLUSIONS

In this paper we introduced briefly a measurement systemwhich allows the testing of nearly all MIMO communica-tions setups currently under discussion. Arbitrary signalscan be generated and transmitted in realistic scenarios. Thisrequires the solution of different estimation problems.

We presented measurements showing the feasibility ofBSS techniques for communications systems under realis-tic conditions. Our setup utilizes BSS as a robust frontendprocessing. This gives us the option to use known SISO es-timation schemes for frequency and timing estimation. Wealso presented a new concept to improve the performance ofa linear BSS by utilizing the finite alphabet of the modulatedsignal in an iterative loop.

6. REFERENCES

[1] Jean-Francois Cardoso and Antoine Souloumiac,“Blind beamforming for non Gaussian signals,” IEEProceedings-F, vol. 140, no. 6, pp. 362–370, December1993.

[2] S.J. Lee, “A New Non-Data-Aided Feedforward Sym-bol Timing Estimator Using Two Samples per Symbol,”IEEE Communications Letters, vol. 6, no. 5, pp. 205–207, May 2002.

[3] P. W. Wolniansky, G. J. Foschini, G. D. Golden, andR. A. Valenzuela, “V-BLAST: An Architecture for Re-alizing Very High Data Rates Over the Rich-ScatteringWireless Channel,” invited paper, Proc. ISSSE-98, 29.September 1998.

[4] Rolf Matzner, “An SNR Estimation Algorithm forComplex Baseband Signals using Higher Order Statis-tics,” FACTA UNIVERSITATIS: ELECTRONICS ANDENERGETICS, vol. 6, no. 1, pp. 41–52, January 1993.

[5] J. Rinas and K.D. Kammeyer, “Comparison of BlindSource Separation under the Application of the TurboPrinciple,” in 5th International ITG Conference onSource and Channel Coding, Erlangen, Germany, Jan-uary 2004, submitted.

3In [5] we present some Monte Carlo Simulations concerning the per-formance of the proposed iterative scheme.