Title: On reducing the communication cost of the diffusion LMS algorithm,
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.