Feedback and Learning in Cognitive Radio Systems

认知无线电是下一代无线系统的关键使能技术，可解决频谱效率、干扰管理和异类网络共存方面的关键挑战。本研究旨在为认知无线电系统开发基础理论和实用算法。它集中在三个关键领域：(I)频谱机会感知和认知；(Ii)频谱机会跟踪和利用；(Iii)认知机会主义网络。这三个领域代表了一个在范围和复杂性上不断扩大的逻辑进程。本研究的主要创新包括在认知无线电系统的设计中利用主要业务过程的重尾和自相似性质。侦听错误的必然性主要是在物理层单独研究的，本研究的所有方面都明确考虑了侦听错误的必然性，从基本性能限制的调查到所有网络层的算法和协议的设计。这项研究的总体目标是严格理解反馈和学习在认知无线电系统中的作用，表征机会性频谱接入的基本结构和性能限制，并开发高效和稳健的方法来利用联网的认知无线电获取频谱机会。综合交通建模、分布式统计推理和共识学习的最新进展，以及动态优化和随机控制的理论和技术，正在开发一种综合方法。这项研究促进了主动学习和发现，并扩大了女学生在工程领域的参与，从K-12女孩到本科生和研究生。

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.