9:30, SPCOM-L11.1
PERFORMANCE ANALYSIS OF A SECOND ORDER STATISTICS BASED SOLUTION FOR THE MIMO CHANNEL IDENTIFICATION PROBLEM
J. XAVIER, V. BARROSO
The CFC2 algorithm is a recently introduced analytical solution for the blind MIMO channel identification problem, provided a certain spectral diversity holds for the stochastic inputs of the MIMO system. Here, we develop a theoretical study to derive the asymptotic performance of the CFC2 algorithm, in terms of mean-square error. Asymptotic normality of the MIMO channel estimate is proved, and the asymptotic error covariance matrix derived. Computer simulation results are included to validate the theoretical expressions.

9:50, SPCOM-L11.2
BOUNDS ON MIMO CHANNEL ESTIMATION AND EQUALIZATION WITH SIDE INFORMATION
B. SADLER, R. KOZICK, T. MOORE
We present constrained Cramer-Rao bounds for multi-input multi-output (MIMO) channel and source estimation. We find the MIMO Fisher information matrix (FIM) and consider its properties, including the maximum rank of the unconstrained FIM, and develop necessary conditions for the FIM to achieve full rank. Equality constraints provide a means to study the potential value of side information, such as training (semi-blind case), constant modulus (CM) sources, or source non-Gaussianity. Nonredundant constraints may be combined in an arbitrary fashion, so that side information may be different for different sources. The bounds are useful for evaluating various MIMO source and channel estimation algorithms. We present an example using the constant modulus blind equalization algorithm.

10:10, SPCOM-L11.3
FINITE-LENGTH MIMO ADAPTIVE EQUALIZATION USING CANONICAL CORRELATIONS
A. DOGANDZIC, A. NEHORAI
We propose finite-length multi-input multi-output adaptive equalization methods for ``smart'' antenna arrays using the statistical theory of canonical correlations. We show that the proposed methods are related to maximum likelihood reduced-rank channel and noise estimation algorithms in unknown spatially correlated noise, and to several recently proposed adaptive equalization schemes.

10:30, SPCOM-L11.4
BLIND EQUALIZATION OF MIMO CHANNELS USING DETERMINISTIC PRECODING
H. ARTES, F. HLAWATSCH
We present a novel precoding or modulation scheme (matrix modulation) that allows parallel transmission of several data signals over an unknown multiple-input multiple-output (MIMO) channel. We first present a theorem on unique signal demodulation and an efficient iterative demodulation algorithm for transmission over an unknown instantaneous-mixture channel. We then generalize our results to an unknown MIMO channel with memory.

10:50, SPCOM-L11.5
BLIND IDENTIFICATION AND EQUALIZATION OF FIR MIMO CHANNELS BY BIDS
Y. HUA, S. AN, Y. XIANG
This paper presents an algorithm of blind identification and equalization of finite-impulse-response and multiple-input and multiple-output (FIR MIMO) channels driven by colored signals. This algorithm is an improved realization of a concept referred to as blind identification via decorrelating subchannels (BIDS). This BIDS algorithm first constructs a set of decorrelators which decorrelate the output signals of subchannels, and then estimates the channel matrix using the transfer functions of the decorrelators, and finally recovers the input signals using the estimated channel matrix. This BIDS algorithm in general assumes that the channel matrix is irreducible and the input signals are mutually uncorrelated and of sufficiently diverse power spectra. However, for channel matrix identification, this BIDS algorithm only requires the channel matrix to be nonsingular and column-wise coprime (which can be non-minimum phase).

11:10, SPCOM-L11.6
THE AUTOCORRELATION MATCHING METHOD FOR DISTRIBUTED MIMO COMMUNICATIONS OVER UNKNOWN FIR CHANNELS
H. LUO, R. LIU, X. LIN, X. LI
The Autocorrelation Matching method is a blind signal separation and channel equalization technique for distributed MIMO communication systems over unknown FIR channels using only second order statistics. This method is based on a theoretical discovery, i.e., under the condition that the autocorrelation functions of the multiple inputs are L-lag independent, an input is recovered, up to a unitary factor and a delay, by an output of an MIMO-FIR equalizer if and only if the autocorrelation function of the output matches that of the input. An optimal zero-forcing equalizer is developed to maximize the SNR for the outputs, i.e., the recovered inputs. Some preliminary simulation results show that the BER in the recovered inputs is about 0.00003 at the SNR = 15 dB. This method has the potential to be applied to cellular wireless communications for the purpose of boosting spectrum efficiency or suppressing co-channel interference.