Sparsity-Aware RF Cartography for Cognitive Networks

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ECCS-1002180Georgios GiannakisUniversity of MinnesotaSparsity-Aware RF Cartography for Cognitive NetworksABSTRACTIntellectual Merit: Wireless cognitive radio (CR) technology holds great promise to address fruitfully the perceived dilemma of bandwidth under-utilization versus spectrum scarcity, which has rendered fixed-access communication networks inefficient. Accordingly, the need arises for fundamental research in critical cognition infrastructure to sense, learn, and adapt to the environment CR networks operate. This proposal aims to develop this infrastructure for comprehensive situation awareness through the novel notion of RF cartography. Paralleling the success of routing tables, the vision is to jointly utilize interference and channel gains maps to identify opportunistically available bands for re-use, and handoff operation; localize and track user activities; as well control resource allocation and routing decisions. The approach draws from contemporary advances in sparsity-aware regression, compressive sampling, basis expansions, spline interpolation, and kriged Kalman filtering. The project leverages these tools to investigate: (a) distributed, online, and adaptive algorithms for map estimation and tracking; (b) training-based and blind cartography options; (c) spatio-temporal spectrum re-use, and localization in the presence of multipath and shadowing effects; and, (d) cartography-driven network utility maximization for optimal cross-layer design of CR networks.Broader Impacts: This research is of interest to software radio designs with IEEE 802.11 compliant standards. In a broader sense, advances in sparsity-aware regression, kriging, splines, and spectrum cartography will permeate benefits to a gamut of areas as diverse as machine learning and data mining for social networks, dynamic magnetic resonance imaging, surveillance using wireless sensor networks, as well as navigation and safety systems. Cognition in networking and localization applications will further provide meaningful experiences to undergraduates and integration of the expertise gained to enhance the content of graduate classes. Outreach to the government and industrial sectors will be possible through short courses, tutorials in workshops and conferences, and student-faculty-staff collaboration.