On reducing the communication cost of the diffusion LMS algorithm

TL;DR

This paper proposes a diffusion LMS algorithm that reduces communication costs in distributed networks with limited energy, maintaining performance and validated through theoretical analysis and large-scale simulations.

Contribution

It introduces a novel diffusion LMS strategy specifically designed to lower communication costs in energy-constrained networks, with comprehensive theoretical and experimental validation.

Findings

Significant reduction in communication costs achieved.

Performance remains comparable to traditional diffusion LMS.

Validated effectiveness through large-scale energy-limited scenario simulations.

Abstract

The rise of digital and mobile communications has recently made the world more connected and networked, resulting in an unprecedented volume of data flowing between sources, data centers, or processes. While these data may be processed in a centralized manner, it is often more suitable to consider distributed strategies such as diffusion as they are scalable and can handle large amounts of data by distributing tasks over networked agents. Although it is relatively simple to implement diffusion strategies over a cluster, it appears to be challenging to deploy them in an ad-hoc network with limited energy budget for communication. In this paper, we introduce a diffusion LMS strategy that significantly reduces communication costs without compromising the performance. Then, we analyze the proposed algorithm in the mean and mean-square sense. Next, we conduct numerical experiments to confirm the theoretical findings. Finally, we perform large scale simulations to test the algorithm efficiency in a scenario where energy is limited.