Distributed average consensus with dithered quantization

作     者：Aysal, Tuncer Can Coates, Mark J. Rabbat, Michael G. 

作者机构：Cornell Univ Sch Elect & Comp Engn Commun Res Signal Proc Grp Ithaca NY 14853 USA McGill Univ Dept Elect & Comp Engn Telecommun & Signal Proc Comp Networks Lab Montreal PQ H3A 2A7 Canada 

出 版 物：《IEEE TRANSACTIONS ON SIGNAL PROCESSING》 (IEEE Trans Signal Process)

年 卷 期：2008年第56卷第10期

页      面：4905-4918页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

主　　题：average consensus distributed algorithms dithering probabilistic quantization sensor networks 

摘      要：In this paper, we develop algorithms for distributed computation of averages of the node data over networks with bandwidth/power constraints or large volumes of data. Distributed averaging algorithms fail to achieve consensus when deterministic uniform quantization is adopted. We propose a distributed algorithm in which the nodes utilize probabilistically quantized information, i.e., dithered quantization, to communicate with each other. The algorithm we develop is a dynamical system that generates sequences achieving a consensus at one of the quantization values almost surely. In addition, we show that the expected value of the consensus is equal to the average of the original sensor data. We derive an upper bound on the mean-square-error performance of the probabilistically quantized distributed averaging (PQDA). Moreover, we show that the convergence of the PQDA is monotonic by studying the evolution of the minimum-length interval containing the node values. We reveal that the length of this interval is a monotonically nonincreasing function with limit zero. We also demonstrate that all the node values, in the worst case, converge to the final two quantization bins at the same rate as standard unquantized consensus. Finally, we report the results of simulations conducted to evaluate the behavior and the effectiveness of the proposed algorithm in various scenarios.