An evolving update interval algorithm for the optimal step-size affine projection algorithm

This paper introduces an evolving update interval algorithm for the optimal step-size affine projection algorithm. The optimal step-size affine projection algorithm is one of numerous approaches to get better performance for the affine projection algorithm. It is suggested by analyzing the mean square deviation of fixed step-size affine projection algorithm. With the optimal step-size affine projection algorithm, in this paper, by evolving the update interval, it is able to show much better performance. From the MSD analysis, the learning curve is dived into two stage: the transient stage and the steady-state. By finding the cross point of affine projection algorithm´s learning curve, the update interval is modified. By updating the weight vector for updated interval, the proposed algorithm reduces the computational complexity. With the proposed algorithm from simulations, it shows higher convergence rate and lower steady-state error.