Reduced-complexity proportionate nlms employing block-based selective coefficient updates

This paper proposes a selective coefficient update algorithm for reducing the complexity of the proportionate normalized least- mean-square (P-NLMS) class of algorithms. It is shown that an optimal subset of coefficients to update, namely those minimizing the a posteriori error, cannot be constructed efficiently. A sub- optimal block-based coefficient selection algorithm is presented that combines proportional weighting of the input signal vector with fast ranking methods. It is compared to existing sub-optimal algorithms with respect to complexity overhead and convergence rate. Simulations show that the proposed algorithm produces performance approaching that of the optimal subset while maintaining a low coefficient selection overhead.