Distributed Parameter Estimation with Quantized Communication via Running Average

TL;DR

This paper introduces a distributed parameter estimation algorithm for sensor networks with quantized data and directed links, using a running average technique to improve convergence to the centralized mean estimate.

Contribution

It presents a novel two-stage algorithm employing running averages to achieve accurate estimation despite quantization and directed communication, outperforming traditional consensus methods.

Findings

Achieves centralized sample mean estimate in mean square and almost sure senses.

Provides convergence rates for the proposed algorithm.

Simulation results demonstrate improved accuracy and robustness.

Abstract

In this paper, we consider the parameter estimation problem over sensor networks in the presence of quantized data and directed communication links. We propose a two-stage algorithm aiming at achieving the centralized sample mean estimate in a distributed manner. Different from the existing algorithms, a running average technique is utilized in the proposed algorithm to smear out the randomness caused by the probabilistic quantization scheme. With the running average technique, it is shown that the centralized sample mean estimate can be achieved both in the mean square and almost sure senses, which is not observed in the conventional consensus algorithms. In addition, the rates of convergence are given to quantify the mean square and almost sure performances. Finally, simulation results are presented to illustrate the effectiveness of the proposed algorithm and highlight the improvements by using running average technique.