NeTS: Small: Toward Distributed Decision Making in Cognitive Radio Ad-hoc Networks Based on Bilevel Equilibrium Programming

NeTS：小型：基于双层均衡规划的认知无线电自组织网络中的分布式决策

• 批准号: 1218717
• 负责人: Gesualdo Scutari
• 金额: $ 40万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218717&HistoricalAwards=false
• 关键词: NeTS; Small; Toward; Distributed; Decision

This project is motivated by the lack of theoretical understanding of distributed control strategies to jointly optimize cognitive radio network functionalities at multiple layers of the networking protocol stack. When physical layer parameters are considered, the resulting optimization problem is often nonconvex, and therefore cannot be solved efficiently in general even through centralized algorithms. The objective of this project is then to look at the cross-layer design of cognitive radio multi-hop networks from a different and novel perspective. The problem of jointly optimizing the sensing parameters, multiple-access scheme, routing, and rate control is formulated as a bi-level equilibrium problem, which can be solved efficiently and in a distributed way. Successful completion of this research will provide the community with a family of novel distributed algorithms that naturally implement vertical and horizontal decompositions across the network. This would represent a major departure from well-understood decomposition algorithms for network utility maximization problems that are known to suffer from slow convergence speed.The project will contribute to the development of novel algorithms and tools that will lay the foundation for the next generation of cognitive networking technology. The impact of the proposed technology to today's society can be as broad and as strong as the potential applications of ad hoc networks; for example, search-and-rescue operations, on-demand communications infrastructure deployment, and intelligent transportation highways and vehicles. The proposed research is integrated with an educational plan designed to train the next-generation of networking professionals, as well as promote cross-fertilization of academic research with industry needs.

该项目的激励是由于缺乏对分布式控制策略的理论理解，无法在网络协议堆栈的多层中共同优化认知无线网络功能。当考虑物理层参数时，最终的优化问题通常是非convex，因此即使通过集中算法也无法有效地解决。 然后，该项目的目的是从不同和新颖的角度来研究认知无线电多跳线网络的跨层设计。共同优化感应参数，多访问方案，路由和速率控制的问题被公式为双层平衡问题，可以有效地和分布式方式解决。这项研究的成功完成将为社区提供一种新颖的分布式算法，这些算法自然会在整个网络上实施垂直和水平分解。对于网络公用事业最大化问题，已知会遭受缓慢的收敛速度的网络实用性最大化问题的理解，这将是一个重大的偏离。该项目将有助于开发新颖的算法和工具，这将为下一代认知网络技术奠定基础。拟议技术对当今社会的影响可能与临时网络的潜在应用一样广泛和强大。例如，搜索操作，按需通信基础设施部署以及智能运输高速公路和车辆。 拟议的研究与一项旨在培训网络专业人士的下一代的教育计划融合在一起，并促进了对行业需求的学术研究的交叉侵入。