SpecEES: Energy-efficient Spectrum and Infrastructure Co-use for Sensing and Communications in Dense Networks

点击翻译按钮获取中文摘要

This project will develop new energy-efficient techniques to co-use wireless spectrum for both communications and sensing. The setting considered will be that of emerging cellular networks consisting of a dense deployment of base-stations (wireless infrastructure on cell towers) providing overlapping wireless coverage. By enabling co-use of spectrum, this research will result in both better connectivity and improved data rates to users, and improved sensing through radar technologies for locating and tracking mobile users. These technologies will thus enable new smart city services and applications. On the educational front, the project will lead to development of new educational materials. Outreach activities to middle and high school students will occur through widely-accessible lecture series. The proposed activities will pay special attention to promoting diversity and nurturing student talent. Finally, the results will be shared with industry through the industrial affiliates program of the Wireless Networking and Communications Group (WNCG) at The University of Texas at Austin. The proposed research is geared at developing a new class of energy efficient cross-layer resource allocation algorithms for joint management of base-station and spectrum resources in dense wireless networks. At the same time, mathematical models and tools based on stochastic geometry will be introduced enabling one to characterize and optimize energy-performance for dense deployments. The intellectual merit can be summarized along four thrusts: (a) Infrastructure and spectrum co-use for communications and radar/sensing, where the focus will be on algorithms for handling interference from a mixture of communications and radar waveforms; (b) Stochastic geometry models and tools for characterizing heterogeneous spectrum use over space, where Central Limit Theorems over space will be developed to characterize SINR and radar distortion performance; (c) Algorithms for mode switching and base-station activation, where load-dependent algorithms for network management will be developed that are cognizant of hysteresis costs associated with changing modes and activation states of base-stations; and (d) Managing user dynamics through directed association to reduce the frequency of change in the network, which will result in improved convergence behavior of management algorithms at various spatial scales.