CIF: Small: Power-constrained distributed vector estimation in wireless sensor networks

无线传感器网络（wsn）有可能对我们日常生活的许多方面产生重大影响，预计在环境监测、健康监测、监视、军事和国土安全应用中至关重要。分布式参数估计是无线传感器网络的主要挑战之一，其中空间分布式电池供电传感器部署在传感领域以监测物理或环境条件。本研究将探讨在传输策略为数位化的情况下，利用一般观测模型对未知向量进行分布估计。本文将解决以下三个问题：(1)硬解码和软解码链路层设计的贝叶斯Cramer-Rao界（CRB）和线性估计。将推导贝叶斯CRB，并开发线性估计器。在硬解码设计中，融合中心（FC）使用恢复的传输离散信息进行估计，而在软解码设计中，FC直接使用连续值信道输出进行估计。研究了观测模型、衰落和通信信道噪声以及调制选择对优化解的影响，并探讨了现有的发射功率、速率和估计精度之间的权衡。对比功率率失真区域将使研究者能够量化软解码（与硬解码相比）链路层设计提供的性能改进；(2)分布式矢量估计中的信道估计问题。研究了无线信道估计误差对分布式信号处理设计的影响。此外，研究者将研究信道估计误差和传输训练符号的能量成本的综合影响如何进一步限制估计精度；(3)基于聚类的数字传输分层网络体系结构中的分布式矢量估计。研究了集群间发射功率分配与估计精度之间存在的权衡。

Wireless sensor networks (WSNs) have the potential to significantly impact many aspects of our everyday life and are anticipated to be critically important for environmental monitoring, health monitoring, surveillance, military, and homeland security applications. Distributed parameter estimation serves as one of the main challenges in WSNs, where spatially distributed battery-powered sensors are deployed over a sensing field to monitor physical or environmental conditions. This research will investigate distributed estimation of an unknown vector with a general observation model, when the transmission strategy is digital. The following three problems will tackled: (1) Bayesian Cramer-Rao bound (CRB) and linear estimators for hard-decoding and soft-decoding link layer designs. The Bayesian CRB will be derived and linear estimators will be developed. In hard-decoding design, the fusion center (FC) uses the recovered transmitted discrete messages for estimation, whereas in soft-decoding design, the FC uses directly the continuous-valued channel outputs for estimation. The impacts of the observation model, fading and communication channel noises and choice of modulation on the optimization solutions will be studied, and the existing tradeoffs between transmit power, rate, and estimation accuracy will be explored. Contrasting the power-rate-distortion regions will enable the investigator to quantify the performance improvement provided by soft-decoding (compared with hard-decoding) link layer design; (2) channel estimation problems in distributed vector estimation. The influence of wireless channel estimation errors on the distributed signal processing designs will be studied. Also, the investigator will study how the combined effects of channel estimation errors and energy cost of transmitting training symbols further limit the estimation accuracy; and, (3) distributed vector estimation in a cluster-based hierarchical network architecture with digital transmission. The existing tradeoffs between transmit power allocation among the clusters and the estimation accuracy will be explored.