Efficient use of sparse adaptive filters

We present a novel adaptive algorithm exploiting the sparseness of an impulse response for network echo cancellation. This sparseness-controlled improved proportionate normalized least mean square (SC-IPNLMS) algorithm is based on IPNLMS which allocates a step-size gain proportional to each filter coefficient. The proposed SC-IPNLMS algorithm achieves improved convergence over IPNLMS by estimating the sparseness of the impulse response and allocating gains for each step-size such that a higher weighting is given to the proportionate term of the IPNLMS for sparse impulse responses. For a less sparse impulse response, a higher weighting will be allocated to the NLMS term. Simulation results presented show improved performance over the IPNLMS algorithm during convergence before and after an echo path change has been introduced. We also discuss the computational complexity of the proposed algorithm.