CAREER: Exploring the Underexplored: A Fundamental Study of Optimal Resource Allocation and Low-Complexity Algorithms in Multi-Radio Multi-Channel Wireless Networks

职业：探索未开发领域：多无线电多通道无线网络中最优资源分配和低复杂度算法的基础研究

• 批准号: 1053777
• 负责人: Yu Cheng
• 金额: $ 40万
• 依托单位: Illinois Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1053777&HistoricalAwards=false
• 关键词: CAREER; Exploring; Underexplored; Fundamental; Study

The multi-radio multi-channel (MR-MC) networking provides a generic computing platform for a wide range of next-generation wireless networks. However, the capacity of MR-MC networking has been underexplored, due to the lack of effective computing methodologies to address the complex multi-dimensional resource allocation. The current state of the art of MR-MC network capacity analysis is limited to heuristic algorithms and loose capacity bound analysis.This project targets at breakthrough studies on optimal resource allocation and low-complexity algorithm development for MR-MC networks. The proposed research activities weave up optimization theory, graph theory, stochastic control, and asymptotic scaling law analysis, to reveal the underpinning factors that distinguish the MR-MC network optimization from the single-radio single-channel (SR-SC) counterpart, develop low-complexity algorithms with theoretically provable performance, and exploit the particular advantages of MR-MC networking in incorporating new techniques for further capacity enhancement. This project will develop a set of novel theoretical tools to fundamentally address the capacity optimization and computational complexity in a multi-dimensional resource space. On the education front, this interdisciplinary research will not only provide various training projects to undergraduate and graduate students, but also inspire students (particularly PhD students) to pursue high-quality research with a creative, open-minded, and cross-disciplinary perspective. On the industry front, the capacity planning and dynamic network control techniques developed in this project are of critical importance to practical wireless network design, and have the potential to be transformed into practical network protocols with low complexity and provable efficiency.

多拉迪奥多通道（MR-MC）网络为广泛的下一代无线网络提供了一个通用计算平台。然而，由于缺乏有效的计算方法来解决复杂的多维资源分配，因此MR-MC网络的能力尚未得到充分激励。 MR-MC网络能力分析的现行现状仅限于启发式算法和宽松的容量约束分析。该项目目标是关于MR-MC网络的最佳资源分配和低复杂性算法开发的突破性研究。提出的研究活动编织了优化理论，图理论，随机控制和渐近缩放级别的法律分析，以揭示将MR-MC网络优化与单拉迪奥单渠道（SR-SC）对应物区分开的基础因素，并在理论上证明了MIR和Advantage and MR-MR-MC CRENTINICE构成MR-MC C-MC C CORNACE INIGICAT和MR-MC C CORTIAME in of MR-MCC cons of STRECTICENT和其他能力的范围。增强。该项目将开发一组新型的理论工具，以从根本上解决多维资源领域的能力优化和计算复杂性。在教育方面，这项跨学科研究不仅将为本科和研究生提供各种培训项目，还可以激发学生（尤其是博士生）以创造性，开放的，开放的，跨学科的观点从事高质量研究。在行业方面，该项目中开发的能力计划和动态网络控制技术对于实用的无线网络设计至关重要，并且有可能将其转变为具有低复杂性和可证明效率的实用网络协议。