NeTS-ProWin: Resource Management and Distributed Protocols for Heterogeneous Cognitive-Radio Networks

NeTS-ProWin：异构认知无线电网络的资源管理和分布式协议

• 批准号: 0627118
• 负责人: Marwan Krunz
• 金额: --
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0627118&HistoricalAwards=false
• 关键词: NeTS; ProWin; Resource; Management; Distributed

The focus of this project is on optimal resource management and control of distributed cognitive radio (CR) networks, with the goal of harvesting the benefits of dynamic spectrum sharing that include improved spatial reuse (i.e., higher network throughput), programmable connectivity, and increased network availability. The research agenda includes centralized analytical formulations that aim at optimizing the operation of the bottom three layers as well as distributed routing and medium access protocols that implement the outcomes of such optimization. Depending on the channel dynamics (e.g., magnitude of the channel coherence time relative to the optimization window), optimizations based on deterministic-control formulations (for slowly varying channels) as well as stochastic-control formulations (for fast varying channels) are considered. In both cases, the underlying setup allows for multi-channel, multi-path routing at either the packet or the session levels. Distributed optimizations are also considered for peer-to-peer (ad hoc) hybrid networks, consisting of cognitive and "legacy" nodes. For this scenario, game-theoretic pricing policies are used to manage the interactions between CR and non-CR nodes for the purpose of achieving optimal routing performance. To validate the findings of this research, the proposed protocols will be implemented on a prototype CR/SDR platform. The proposed optimization framework provides a general methodology that can be applied to significantly improve the performance of resource constrained networks. Given the widespread use of wireless devices, this research is expected to have a strong impact on the performance of many wireless applications, including sensor networks, mesh networks, military radios, wireless LANs, etc.

该项目的重点是分布式认知无线电（CR）网络的最佳资源管理和控制，目标是收获动态频谱共享的好处，包括改进的空间重用（即，更高的网络吞吐量）、可编程连接性以及更高的网络可用性。研究议程包括集中的分析配方，旨在优化操作的底部三层，以及分布式路由和介质访问协议，实现这种优化的结果。取决于信道动态（例如，相对于优化窗口的信道相干时间的幅度），考虑基于确定性控制公式（对于缓慢变化的信道）以及随机控制公式（对于快速变化的信道）的优化。在这两种情况下，底层设置允许在数据包或会话级别进行多通道、多路径路由。分布式优化也被认为是对等（ad hoc）的混合网络，由认知和“传统”节点。对于这种情况下，博弈论的定价策略被用来管理CR和非CR节点之间的相互作用，以实现最佳的路由性能。为了验证这项研究的结果，提出的协议将在原型CR/SDR平台上实现。建议的优化框架提供了一个通用的方法，可以应用于显着提高资源受限网络的性能。鉴于无线设备的广泛使用，这项研究预计将对许多无线应用的性能产生强烈的影响，包括传感器网络，网状网络，军用无线电，无线局域网等。