Feedback and Learning in Cognitive Radio Systems

点击翻译按钮获取中文摘要

Cognitive radio is the key enabling technology for future generations of wireless systems that address critical challenges in spectrum efficiency, interference management, and coexistence of heterogeneous networks. This research aims to develop fundamental theories and practical algorithms for cognitive radio systems. It focuses on three key areas: (i) spectrum opportunity sensing and cognition; (ii) spectrum opportunity tracking and exploitation; and (iii) cognitive opportunistic networking. These three areas represent a logical progression that expands in scope and complexity.Key innovations of this research include exploiting the heavy tail and self similar nature of primary traffic processes in the design of cognitive radio systems. The inevitability of sensing errors, which has mainly been studied in isolation at the physical layer, is explicitly taken into account in all aspects of this research, from the investigation of fundamental performance limits to the design of algorithms and protocols at all network layers. The overall objective of this research is to gain a rigorous understanding of the role of feedback and learning in cognitive radio systems, to characterize fundamental structures and performance limits of opportunistic spectrum access, and to develop efficient and robust approaches to harvest spectrum opportunities using networked cognitive radios. An integrative approach is being developed, integrating recent advances in traffic modeling, distributed statistical inference and consensus learning, and theories and techniques of dynamic optimization and stochastic control. This research promotes active learning and discovery and broadens participation of female students in engineering, from K-12 girls to undergraduate and graduate students.