Unsupervised diffusion-based LMS for node-specific parameter estimation over wireless sensor networks

TL;DR

This paper introduces an unsupervised diffusion LMS algorithm for wireless sensor networks that enables nodes to accurately estimate their specific parameters without prior knowledge of shared interests, improving estimation bias and efficiency.

Contribution

The paper proposes a novel unsupervised diffusion LMS method that automatically identifies relevant neighbor estimates, reducing bias in node-specific parameter estimation in wireless sensor networks.

Findings

Theoretical analysis of asymptotic bias in blind estimation scenarios.

The proposed algorithm achieves unbiased estimates of node-specific parameters.

Simulation results demonstrate the effectiveness of the method.

Abstract

We study a distributed node-specific parameter estimation problem where each node in a wireless sensor network is interested in the simultaneous estimation of different vectors of parameters that can be of local interest, of common interest to a subset of nodes, or of global interest to the whole network. We assume a setting where the nodes do not know which other nodes share the same estimation interests. First, we conduct a theoretical analysis on the asymptotic bias that results in case the nodes blindly process all the local estimates of all their neighbors to solve their own node-specific parameter estimation problem. Next, we propose an unsupervised diffusion-based LMS algorithm that allows each node to obtain unbiased estimates of its node-specific vector of parameters by continuously identifying which of the neighboring local estimates correspond to each of its own estimation tasks. Finally, simulation experiments illustrate the efficiency of the proposed strategy.