Frequency-Domain Diffusion Bias-Compensated Adaptation With Periodic Communication

Abstract

When the input signal of each node is interfered by noise, the distributed frequency-domain adaptive algorithm yields biased estimation. To eliminate the noise-induced bias with reduced communication load, this article proposes the frequency-domain diffusion bias-compensated adaptive filtering with periodic communication. By minimizing the bias-eliminating cost function, the frequency-domain diffusion bias-compensated LMS (FD-BCLMS) is first derived. Subsequently, to achieve lower computational complexity and communication cost, we design the double periodic FD-BCLMS (DPFD-BCLMS) algorithm by resorting to periodic update and communication strategies. Moreover, the DPFD-BCLMS with power normalized scheme (DPFD-BCNLMS) is developed to improve the convergence rate in the case of colored input. The transient and steady-state behaviors are investigated. For the steady-state performance degradation in the DPFD-BCNLMS, we modify the combination step near steady-state, resulting in the switched DPFD-BCNLMS (SDPFD-BCNLMS). A new estimation method for the input noise variance is also provided. Finally, the superiority of the proposed algorithms is validated by numerical simulations.