Distributed Estimation for Adaptive Networks Based on Serial-Inspired Diffusion

TL;DR

This paper introduces a serial-inspired diffusion algorithm for distributed estimation in networks, leveraging message-passing strategies to enhance performance by utilizing the most recent information in the network.

Contribution

It proposes a novel serial-inspired diffusion approach that improves distributed estimation performance by integrating message-passing strategies with diffusion algorithms.

Findings

Significant performance improvement over existing diffusion algorithms.

Effective utilization of the most recent information in the network.

Enhanced robustness and efficiency in distributed estimation.

Abstract

Distributed estimation and processing in networks modeled by graphs have received a great deal of interest recently, due to the benefits of decentralised processing in terms of performance and robustness to communications link failure between nodes of the network. Diffusion-based algorithms have been demonstrated to be among the most effective for distributed signal processing problems, through the combination of local node estimate updates and sharing of information with neighbour nodes through diffusion. In this work, we develop a serial-inspired approach based on message-passing strategies that provides a significant improvement in performance over prior art. The concept of serial processing in the graph has been successfully applied in sum-product based algorithms and here provides inspiration for an algorithm which makes use of the most up-to-date information in the graph in combination with the diffusion approach to offer improved performance.