On second-order statistics-based blind equalization of FIR/IIR multiple-input multiple-output channels with common zeros

The problem of blind equalization of MIMO (multiple-input nultiple-output) communications channels is considered using the second-order statistics of the data. Such models arise when a single receiver data from multiple sources is fractionally sampled (assuming that there is excess bandwidth), or when an antenna array is used with or without fractional sampling. We focus on direct design of finite-length MMSE (minimum mean-square error) blind equalizers. We allow infinite impulse response (IIR) channels. Our approaches also work when the "subchannel" transfer functions have common zeros so long as the common zeros are minimum-phase zeros. We only require that the there exist a causal, stable left inverse (not necessarily unique) to the MIMO transfer function and that the leading coefficient matrix of the MIMO channel impulse response have its rank equal to the number of sources. The channel length or model orders need not be known. The sources are recovered up to a unitary mixing matrix and are further 'unmixed' using higher-order statistics of the data. An illustrative simulation example is provided.