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

CIF：SMALL：无线网络中时空变化的信道映射估计和跟踪

• 批准号: 1526908
• 负责人: Athina Petropulu
• 金额: $ 50万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526908&HistoricalAwards=false
• 关键词: CIF; SMALL; Spatiotemporally; Varying; Channel

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.

无线信道映射的可用性可以极大地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很有价值，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，这些应用将支持改进的无线性能。在现实环境中，无线媒体的统计数据在时间和空间上动态变化。该研究开发了基于网络节点获得的信道测量来构建地理区域上的无线信道映射的理论和算法。信道的描述性统计，在这里被称为信道状态，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变。信道状态包含路径损耗指数、阴影功率和相关距离，并且对网络节点隐藏；节点只能观察到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、非线性、时变和可能的非平稳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。通过丰富的非线性滤波和随机控制理论对估计问题进行了探讨。研究了以下几个问题：(1)分散的信道跟踪和时空信道预测；(2)用于高效信道采样的事件触发采样；(3)非平稳信道的结构化随机模型。该项目有一个实验组件，用于通知分析模型，并用于测试/评估所开发的方法。该项目吸引了研究生和本科生学习一系列理论科目，并在WINLAB？S通信试验台上进行了测量。