VECTORIZATION OF THE DLMS TRANSVERSAL ADAPTIVE FILTER

The subject of high sampling rate realizations for transversal adaptiv e filters is addressed. In particular, a vectorized version of the del ayed least mean squares (DLMS) algorithm is derived using lookahead co mputation techniques. The resulting parallel algorithm is then mapped onto a linear array of highly pipelined processing modules, which can accept an input vector of arbitrary length, and compute the correspond ing output vector in a single clock cycle. The proposed system is show n to be capable of implementing transversal adaptive filters at sampli ng rates which are theoretically without bound. The performance of the proposed system is analyzed and simulation results are presented to v erify the convergence properties of the algorithm under varying degree s of vectorization.