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.

该项目的动机是缺乏对分布式控制策略的理论理解，以在网络协议栈的多个层联合优化认知无线电网络功能。当考虑物理层参数时，产生的优化问题通常是非凸的，因此即使通过集中式算法通常也无法有效解决。 该项目的目标是从不同且新颖的角度看待认知无线电多跳网络的跨层设计。联合优化感知参数、多址方案、路由和速率控制的问题被表述为双层均衡问题，可以以分布式方式高效求解。这项研究的成功完成将为社区提供一系列新颖的分布式算法，这些算法可以自然地在网络上实现垂直和水平分解。这将代表着与众所周知的网络效用最大化问题分解算法的重大背离，这些问题已知收敛速度慢。该项目将有助于开发新颖的算法和工具，为下一代认知奠定基础。网络技术。所提出的技术对当今社会的影响可能与自组织网络的潜在应用一样广泛和强大；例如，搜索和救援行动、按需通信基础设施部署以及智能交通高速公路和车辆。 拟议的研究与旨在培训下一代网络专业人员的教育计划相结合，并促进学术研究与行业需求的交叉融合。