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

该项目的重点是分布式认知无线电(CR)网络的最优资源管理和控制，目标是获得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)、可编程连接和更高的网络可用性。研究议程包括旨在优化最低三层操作的集中式分析公式，以及实施这种优化结果的分布式路由和介质访问协议。根据信道动态(例如，相对于优化窗口的信道相干时间的大小)，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，底层设置都允许在分组或会话级别进行多通道、多路径路由。分布式优化也被考虑用于对等(特别)混合网络，该混合网络由认知节点和“遗留”节点组成。在这种情况下，使用博弈论定价策略来管理CR和非CR节点之间的交互，以实现最优的路由性能。为了验证本研究的发现，所提出的协议将在原型CR/SDR平台上实现。所提出的优化框架提供了一种通用的方法，可用于显著提高资源受限网络的性能。鉴于无线设备的广泛使用，这项研究有望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。

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.