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.
|Effective start/end date||9/1/15 → 8/31/18|
- National Science Foundation (National Science Foundation (NSF))