Distributed Parameter Estimation with Quantized Communication via Running Average
For sensor networks requiring distributed estimation with communication constraints, this work offers a method that provably matches centralized performance, improving upon conventional consensus algorithms.
The paper proposes a two-stage distributed algorithm for parameter estimation over sensor networks with quantized data and directed links, using a running average technique to achieve the centralized sample mean estimate in mean square and almost sure senses, with convergence rates provided.
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.