Convergence of stochastic-approximation-based algorithms for blind channel identification

We develop adaptive algorithms for multichannel (single-input-multiple-output, or SIMO) blind identification with both statistic and deterministic models. In these algorithms, the estimates are continuously improved while receiving new signals. Therefore, the algorithms can track the channel continuously and thus are amenable to real applications such as wireless communications. At each step, only a small amount of computation is involved. The algorithms are based on stochastic-approximation methods. The convergence properties of these algorithms are proved. Simulation examples are presented to show the performance of the algorithms.