Deterministic Blind Subspace MIMO Equalization
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Deterministic Blind Subspace MIMO Equalization Balaji Sampath Electrical and Computer Engineering Department and Institute for Systems Research, University of Maryland, College Park, MD 20742, USA Email: [email protected]
K. J. Ray Liu Electrical and Computer Engineering Department and Institute for Systems Research, University of Maryland, College Park, MD 20742, USA Email: [email protected]
Ye (Geoffrey) Li School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA Email: [email protected] Received 25 October 2001 and in revised form 14 February 2002 A subspace based approach for the blind multiple signal separation and recovery for MIMO systems is proposed in this paper. Instead of using the statistics of the received signal, the proposed algorithm exploits the received signal structure and the finite alphabet property of the desired signals. The finite alphabet property is used to remove the unknown unitary matrix that is associated with most of the statistics-based MIMO system identification algorithms. The proposed algorithm also incorporates an error-correcting procedure; therefore, it has more accuracy than the existing algorithms. Computer simulation results demonstrate that the algorithm can detect the signals and estimate channel parameters accurately with very few symbols, even under high noise and bad channel conditions. Keywords and phrases: blind signal processing, subspace algorithm, MIMO system, equalization.
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INTRODUCTION
Multiple transmit and receive antennas can be used in wireless communications to form multiple-input and multipleoutput (MIMO) systems to improve transmission capacity and performance. The first problem that we have to address before using MIMO communication systems is to identify and equalize MIMO systems, that is, to find system parameters, separate and recover signals. In this paper, we present blind subspace algorithm for MIMO system equalization. Since almost all MIMO systems can be modeled or approximated as FIR systems, we limit ourselves to linear FIR systems. A number of algorithms have been proposed for blind identification and equalization of channels with only one input. Traditionally, most blind equalization algorithms for single-input and single-output (SISO) systems have been based on higher-order statistics [1, 2, 3, 4]. In the last few years, a number of second-order statistics based algorithms have been proposed in [5, 6, 7] to exploit the cyclostationarity of oversampled continuous SISO communication
systems. Since an oversampled continuous SISO system is equivalent to a discrete single-input and multiple-output (SIMO) system, these algorithms can be regarded as addressing the SIMO system identification problem as well. Recently, a number of subspace-based algorithms have been proposed in [8, 9, 10, 11, 12] for blind system identification. In particular, we have proposed a new deterministic subspace-based algorithm in [11, 12] which addresses blind equalization of oversampled continuous SISO (or equivalent
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