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分布式控制策略的理论理解，以共同优化认知无线电网络功能的网络协议栈的多个层。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也不能有效地解决。 这个项目的目的是从一个不同的和新颖的角度来看认知无线电多跳网络的跨层设计。联合优化的传感器参数，多址接入方案，路由和速率控制的问题被制定为一个双层均衡问题，可以有效地解决，并在一个分布式的方式。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，这些算法可以在整个网络中自然地实现垂直和水平分解。这将是一个重大的背离，从众所周知的网络效用最大化问题的分解算法，已知的是遭受缓慢的收敛速度。该项目将有助于开发新的算法和工具，这将奠定下一代认知网络技术的基础。所提出的技术对当今社会的影响可以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公路和车辆。 拟议的研究与旨在培养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。