Diffusion bias-compensated LMS estimation for multitask adaptive networks
In this paper, we study the problem of the least mean-square algorithm based on bias compensation in multitask diffusion adaptive networks. Nodes in networks are divided into different clusters and the nodes in the same cluster cooperatively estimate a common parameter. When regressors are corrupted by additive white noise, the estimate results of the traditional multitask diffusion least mean-square (Multi-LMS) algorithm are biased. In order to obtain the unbiased estimation, we propose two multitask diffusion bias-compensated least mean-square (Multi-BCLMS) algorithms by achieving the real-time estimation of the input noise variance, which can be denoted by Multi-BCLMS-A and Multi-BCLMS-B respectively. Simulation results show that the two algorithms perform better than the Multi-LMS algorithm in estimation accuracy and mean-square error. Furthermore, the second algorithm (Multi-BCLMS-B) is simpler to implement and the transient is faster than the first one (Multi-BCLMS-A).