Performance analysis of diffusion LMS algorithm for cyclostationary inputs

• Published in: Signal processing Vol. 150; pp. 33 - 50
• Main Authors: Wang, Wenyuan, Zhao, Haiquan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2018
• Subjects: Adaptive network; Cyclostationary signal; Diffusion LMS; Energy conservation; Cyclostationary signal; Adaptive network; Energy conservation; Diffusion LMS
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2018.03.019

•This paper analyzes the performance of the diffusion least mean square (DLMS) algorithm for cyclostationary.•The transient performance analysis of the DLMS algorithm with cyclostationary input is conducted.•The steady state behavior of DLMS algorithm for cyclostationary input is also investigated.•The stability analysis of the DLMS algorithm is provided.•we analyze the robustness of the DLMS algorithm in the H∞ sense for cyclostationary inputs over network.•This paper also provides the bound of convergence time.•Extensive simulations are carried out to verify the results of analysis presented in this paper. As a well-known non-stationary signal, the cyclostationary white Gaussian signal widely exists in many practical applications, which is defined as a particular Gaussian noise whose autocorrelation function is cyclically time-varying. This paper presents the performance analysis of the diffusion least mean squares (DLMS) algorithms in distributed networks while the input signals are the cyclostationary white Gaussian process. We analyze mean and mean square behaviors of the DLMS algorithm. It is found that the time-variations of steady-state mean square deviation (MSD) of DLMS algorithm can be ignored when the input signals have the fast variations of input autocorrelation function. Moreover, the robustness of the DLMS algorithm in the H∞ sense for cyclostationary inputs over networks is analyzed. The bound of convergence time of the DLMS algorithm for cyclostationary white Gaussian inputs is also provided in this paper. The simulated results show the validity of the analytical results.