Data-Reserved Periodic Diffusion LMS With Low Communication Cost Over Networks

In this paper, we analyze diffusion strategies in which all nodes attempt to estimate a common vector parameter for achieving distributed estimation in adaptive networks. Under diffusion strategies, each node essentially needs to share processed data with predefined neighbors. Although the use of internode communication has contributed significantly to improving convergence performance based on diffusion, such communications consume a huge quantity of power in data transmission. In developing low-power consumption diffusion strategies, it is very important to reduce the communication cost without significant degradation of convergence performance. For that purpose, we propose a data-reserved periodic diffusion least-mean-squares (LMS) algorithm in which each node updates and transmits an estimate periodically while reserving its measurement data even during non-update time. By applying these reserved data in an adaptation step at update time, the proposed algorithm mitigates the decline in convergence speed incurred by most conventional periodic schemes. For a period p, the total cost of communication is reduced to a factor of 1/p relative to the conventional adapt-then-combine (ATC) diffusion LMS algorithm. The loss of combination steps in this process leads naturally to a slight increase in the steady-state error as the period p increases, as is theoretically confirmed through mathematical analysis. We also prove an interesting property of the proposed algorithm, namely, that it suffers less degradation of the steady-state error than the conventional diffusion in a noisy communication environment. Experimental results show that the proposed algorithm outperforms related conventional algorithms and, in particular, outperforms ATC diffusion LMS over a network with noisy links.11Ysciescopu

Distributed Estimation of Spatially Varying Distributed Parameter System

Adaptive filter shows a significant role in the field of digital signal processing and wireless communication. It integrates LMS algorithm in real time situations because of its low computational complexity and simplicity. The adaptive distributed strategy is built on the diffusion cooperation scheme among nodes at different locations that are dispersed over a wide topographical area. Computations have been performed in all the nodes and all the results are shared among them so as to obtain precise parameters of interest. There are some scenarios where estimation parameters vary over both space and time domains across the network. A set of basis functions i.e. Chebyshev polynomials is used to describe the space-varying nature of the parameters and diffusion least mean-squares strategy is proposed to recover these parameters. The parameters of our concern are assessed for both one dimensional and two dimensional networks. Stability and convergence of the proposed algorithm have been analysed and expressions are derived to predict the behavior. Network stochastic matrices are used to combine exchanged information between nodes. The results show that the performances of the networks also depend upon the combination matrices. The resulting algorithm is distributed, co-operative and able to respond to the real time changes in environmen