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.

无线网络最近最重要的进展之一是认知无线电网络（CRN），它可以允许未授权（或次要）用户访问分配给授权（主要）用户的频段，而不会破坏其性能。研究发现，CRN 可以显着提高频谱利用率。CRN 的基本原理是首先感知主要用户的频谱使用情况，然后机会性地将功率级别和信道分配给次要用户，以便主要用户的干扰水平在可接受的阈值内。 此类网络中协议的大多数理论分析都使用基于磁盘/图形的近似（其中“邻近”链路不能同时传输）来模拟无线干扰；然而，这些都是不够的，并且可能导致不可行的解决方案，在主要用户处具有不可接受的干扰水平。该提案的目标是研究物理干扰模型中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地近似干扰。 由于干扰起着核心作用，该提案的结果将有助于为更广泛的无线网络领域提供理论基础，而不仅仅是 CRN 的应用。