Bilinear approach to multiuser second-order statistics-based blind channelestimation

We present a bilinear approach to mutiple-input multiple-output (MIMO) blin d channel estimation where products of channel parameters are first estimat ed from the covariance of the received data, The channel parameters are the n obtained as the dominant eigenvectors of the outer-product estimate. Nece ssary and sufficient identifiability conditions are presented for a single channel and extended to the multichannel case, It is found that this techni que can identify the channel to within a subspace ambiguity, as long as the basis functions for the channel satisfy certain constraints, regardless of the left invertability of the channel matrix. One important requirement fo r identifiability is that the number of channel parameters is small compare d with the channel length; advantageously, this is exactly the situation in which this algorithm has significantly lower complexity than competing (pa rametric, multiuser) blind algorithms. Simulations show that the technique is applicable in situations where typical identifiability conditions fail: common nulls, a single symbol-spaced channel, and more users than channels. These simulations are :: for the "almost flat" faded situation when the pr opagation delay spread is a fraction of the transmission pulse duration las might : be found in current TDMA systems), Comparisons are made,.,when pos sible, to a subspace method incorporating knowledge of the basis functions. The bilinear approach requires significantly less computation but performs better than the subspace method at low SNR, especially for multiple users.