Distributed Diffusion-Based LMS for Node-Specific Adaptive Parameter Estimation

TL;DR

This paper introduces a distributed diffusion-based LMS algorithm that enables nodes in a network to estimate local, common, and global parameters efficiently, with proven convergence and steady-state performance, validated through simulations in cognitive radio networks.

Contribution

It presents a novel diffusion-based LMS algorithm for node-specific parameter estimation, accommodating multiple parameter types with convergence analysis and performance evaluation.

Findings

Algorithm is asymptotically unbiased.

Steady-state performance is characterized by energy conservation.

Simulations confirm effectiveness in cognitive radio spectrum sensing.

Abstract

A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where nodes are interested in estimating parameters of local interest, parameters of common interest to a subset of nodes and parameters of global interest to the whole network. To address the different node-specific parameter estimation problems, this novel algorithm relies on a diffusion-based implementation of different Least Mean Squares (LMS) algorithms, each associated with the estimation of a specific set of local, common or global parameters. Coupled with the estimation of the different sets of parameters, the implementation of each LMS algorithm is only undertaken by the nodes of the network interested in a specific set of local, common or global parameters. The study of convergence in the mean sense reveals that the proposed algorithm is asymptotically unbiased. Moreover, a spatial-temporal energy conservation relation is provided to evaluate the steady-state performance at each node in the mean-square sense. Finally, the theoretical results and the effectiveness of the proposed technique are validated through computer simulations in the context of cooperative spectrum sensing in Cognitive Radio networks.