CAREER: Cross-layer optimization in Cognitive Radio Networks in the Physical interference model based on SINR constraints: Algorithmic Foundations

职业：基于 SINR 约束的物理干扰模型中认知无线电网络的跨层优化：算法基础

• 批准号: 0845700
• 负责人: Anil Kumar Vullikanti
• 金额: $ 45万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-02-15 至 2015-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845700&HistoricalAwards=false
• 关键词: CAREER; Cross; layer; optimization; Cognitive

One of the most significant recent advances in wireless networks is theCognitive Radio Network (CRN), which can allow unlicensed (or secondary) users to access spectrum bands allocated to licensed (primary) users, without disrupting their performance.Since many licensed spectrum bands have been found to be greatly underutilized,CRNs can potentially enhance the spectrum usage significantly.The basic principle underlying CRNs is to first sense the spectrum usage by primary users, and then allocate power levels and channels opportunistically to the secondary users, so that the interference levels at primary users are within an acceptable threshold. Most theoretical analyses of protocols in such networks use disk/graph based approximations (in which "close-by" links cannot transmit simultaneously) to model wireless interference; however, these are inadequate and can lead to infeasible solutions with unacceptable interference levels at the primary users.The goal of this proposal is to examine the theoretical foundations of cross-layer optimization in Cognitive Radio Networks in the Physical interference model, which is considered a much better approximation of interference than disk based models. The results of this proposal will contribute to the theoretical underpinnings of the broader area of wireless networks, not just the application of CRN, because of the central role interference plays.

无线网络中最重要的进展之一是认知无线电网络(CRN)，它允许未经许可的(或次要的)用户在不影响其性能的情况下访问分配给许可(主要)用户的频谱。由于许多许可的频段被发现严重未得到充分利用，CRN潜在地可以显著地提高频谱利用率。CRN的基本原理是首先感知主要用户的频谱使用情况，然后机会性地为次要用户分配功率水平和信道，从而使主要用户的干扰水平在可接受的阈值内。在这类网络中，大多数协议的理论分析都使用基于磁盘/图的近似(在这种情况下“邻近”链路不能同时传输)来模拟无线干扰；然而，这些都是不充分的，并且可能导致在主用户具有不可接受的干扰水平的不可行解。该建议的目标是在物理干扰模型中检验认知无线电网络中跨层优化的理论基础，该模型被认为是比基于磁盘的模型更好的干扰近似。这项提议的结果将有助于更广泛的无线网络领域的理论基础，而不仅仅是CRN的应用，因为干扰起着核心作用。