Collaborative Research: NeTS-NBD: Optimal Channel-Aware Access Control in Multi-hop Wireless Networks

合作研究：NeTS-NBD：多跳无线网络中的最优信道感知访问控制

• 批准号: 0626620
• 负责人: Anthony Ephremides
• 金额: $ 10万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-15 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0626620&HistoricalAwards=false
• 关键词: Collaborative; Research; NeTS; NBD; Optimal

This project aims at developing a formal methodology for analyzing channel-aware random access in wireless networks. The unique features of this effort include: the development of models that incorporate, amongst other parameters, channel state as a central aspect of their formulation, and the use of cross-cutting tools to analyze the performance of such networks. The mathematical backbone from which the model and resulting solutions arise in this project are optimization and game theory. The specific results that will emerge during the course of this effort include: models that merge channel awareness and capture, a better understanding of the use of opportunism in random access, and finally, an understanding of the ways in which degrees of freedom can be allocated in multiple-antenna random access.The impact of the underlying physical medium on the performance of access schemes can be considerable, particularly in wireless settings where the channel is highly dynamic and is impacted by myriad changes in the surrounding environment. Thus, a succinct representation of the physical medium combined with sophisticated analytical techniques to tackle the resulting models are essential to tap into the gains resulting from the use of channel-awareness in random access.

该项目旨在开发一种用于分析无线网络中频道感知的随机访问的正式方法。这项工作的独特功能包括：开发模型的开发，这些模型在其他参数中，渠道状态作为其配方的中心方面，以及使用跨切割工具来分析此类网络的性能。该项目中出现模型和结果解决方案的数学骨干是优化和游戏理论。在这项工作过程中将出现的具体结果包括：合并渠道意识和捕获的模型，更好地理解机会主义在随机访问中的使用，以及对自由度的理解的理解，可以在多个安特纳纳随机访问中分配的自由度的方式。在无线方面的影响很大，在频道的情况下，对频道的影响很大，并且在频道上会发生多大变化。 环境。因此，物理介质的简洁表示与精致的分析技术相结合，以应对所得模型，对于在随机访问中使用通道意识而导致的收益至关重要。