Variable step-size normalized subband adaptive filtering algorithm for self-interference cancellation

Abstract

Self-interference (SI) is usually generated by the simultaneous transmission and reception of the same system, which has a negative effect on the subsequent processing of the received signal. To solve this problem, a variable step-size normalized subband adaptive filtering (NSAF) algorithm is proposed to eliminate the SI. First, the optimal weight vector of each subband is obtained based on minimizing the mean-square deviation between the optimal weight vector and its estimation, and then the power of the residual SI is estimated by the shrinkage method. To eliminate the influence of input noise estimation bias on the algorithm, we estimate the input noise power of each subband and replace the fixed input noise power at each iteration. Then, the convergence, steady-state behavior and computational complexity are analyzed to prove the feasibility of the proposed algorithm. Simulation results demonstrate that the proposed algorithm is superior to cited algorithms for SI cancellation. Moreover, for the direction of arrival estimation, the received signal processed by the proposed algorithm is more accurate than those cited algorithms.