On reducing the communication cost of the diffusion LMS algorithm

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/24/2020

Theoretical analysis and simulation methods for Hawkes processes and their diffusion approximation

Oscillatory systems of interacting Hawkes processes with Erlang memory k...
research
02/28/2018

An Event-based Diffusion LMS Strategy

We consider a wireless sensor network consists of cooperative nodes, eac...
research
08/06/2016

Weighted diffusion LMP algorithm for distributed estimation in non-uniform noise conditions

This letter presents an improved version of diffusion least mean ppower ...
research
07/29/2015

Diffusion Adaptation Over Clustered Multitask Networks Based on the Affine Projection Algorithm

Distributed adaptive networks achieve better estimation performance by e...
research
09/29/2015

Censoring Diffusion for Harvesting WSNs

In this paper, we analyze energy-harvesting adaptive diffusion networks ...
research
04/23/2018

Deterministic and Randomized Diffusion based Iterative Generalized Hard Thresholding (DiFIGHT) for Distributed Sparse Signal Recovery

In this paper, we propose a distributed iterated hard thresholding algor...
research
10/06/2021

Hypothesis Testing of One-Sample Mean Vector in Distributed Frameworks

Distributed frameworks are widely used to handle massive data, where sam...

Please sign up or login with your details

Forgot password? Click here to reset