Scheduled Step-Size Subband Adaptive Filter Algorithm With Implemental Consideration

Abstract

This article proposes a scheduled step-size normalized subband adaptive filter (NSAF) algorithm in stationary system environment. The mean-square deviation of the NSAF according to the step size is analyzed geometrically to construct a pre-designed trajectory. The mean-square deviation learning curve of the NSAF algorithm is forced to follow the pre-designed trajectory. This method removes the need for the NSAF algorithm to introduce tuning parameters and does not add any additional online computation. The table of the scheduled step sizes can be reconstructed online in proportion to not only the number of taps but also the number of subbands once they are scheduled offline. The novel memory-efficient scheduling scheme minimizes the memory space required and simplifies operation without performance degradation. Because of these features, the proposed algorithm performs as well as the variable-step-size NSAFs studied previously, and is very suitable for chip level implementation in terms of computational complexity and memory space. Simulation results show that the proposed algorithm is robust against external environment change and has good performance compared to the existing variable step-size algorithms without any additional online computation and tuning parameter.