CIF: SMALL: Spatiotemporally Varying Channel Map Estimation and Tracking in Wireless Networks

无线信道映射的可用性可以大大提高无线网络的性能和可靠性。除了依赖于信道信息的传统应用之外，信道映射在新兴应用中也是有价值的，例如通信感知运动和路径规划、网络路由、连接维护和动态覆盖，这将支持改进的无线性能。在现实环境中，无线介质的统计信息在时间和空间上动态变化。本研究开发了基于网络节点获得的信道测量在地理区域上建立无线信道地图的理论和算法，信道的描述性统计，这里称为信道状态，被建模为离散时间随机过程，根据完全或部分已知的统计模型在时间或空间上演化。信道状态包含路径损耗指数、阴影功率和相关距离，并且对网络节点是隐藏的;节点只能观察它们各自的信道实现。该项目开发了一种新的框架，用于在复杂的、非线性演化的、时变的和可能非平稳的环境中对信道状态和信道幅度进行动态时空估计/跟踪/预测。利用非线性滤波和随机控制的丰富理论来处理估计问题。研究了几个问题，包括（1）分散式信道跟踪时空信道预测，（2）事件触发采样的有效信道采样，（3）非平稳信道的结构化随机模型。该项目有一个实验组成部分，为分析模型提供信息，并用于测试/评估所开发的方法。该项目从事研究生和本科生在一系列的理论课题，也测量WINLAB？的通信测试平台。

The availability of wireless channel maps can greatly improve the performance and reliability of wireless networks. In addition to traditional applications which depend on channel information, channel maps can be valuable in emerging applications such as communication-aware motion and path planning, network routing, connectivity maintenance and dynamic coverage, which will support improved wireless performance. In a realistic setting, the statistics of the wireless medium change dynamically in time and space. This research develops theory and algorithms for building wireless channel maps over a geographical area based on channel measurements obtained by the network nodes.The descriptive statistics of the channel, referred to here as the channel state, are modeled as discrete time stochastic processes, evolving in time or space according to a fully or partially known statistical model. The channel state encompasses the path-loss exponent, the shadowing power and the correlation distance, and is hidden from the network nodes; the nodes can only observe their respective channel realizations. This project develops a novel framework for dynamic spatiotemporal estimation / tracking / prediction of both the channel state and the channel magnitude, in complex, nonlinearly evolving, time varying and possibly nonstationary environments. The estimation problem is approached through the rich theory of nonlinear filtering and stochastic control. Several issues are studied, including (1) Decentralized channel tracking & spatiotemporal channel prediction, (2) Event triggered sampling for efficient channel sampling, (3) Structured stochastic models for nonstationary channels. The project has an experimental component, which informs the analytical models and is also used to test/evaluate the developed methods. The project engages graduate and undergraduate students in a range of theoretical subjects and also measurements performed on WINLAB?s communications testbed.