Bias-compensated LMS algorithm for sparse systems over adaptive network
We propose bias-compensated algorithms based on the RZA-LMS algorithm and diffusion RZA-LMS algorithm. Our proposed algorithms improve the accuracy of estimation under the situation that input of the adaptive filter contains noise. Estimation methods of the input noise' variance are given for implementing our single-node and diffusion bias-compensated algorithms. Simulation results show that the proposed algorithms have better accuracy than algorithms without bias-compensation and the estimation results are unbiased under different noise levels.