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.

多无线电多信道（MR-MC）网络为下一代无线网络提供了一个通用的计算平台。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问题，MR-MC网络的能力尚未得到充分的开发。目前MR-MC网络容量分析仅限于启发式算法和松散容量界分析。本项目旨在对MR-MC网络的最优资源分配和低复杂度算法开发进行突破性研究。提出的研究活动将优化理论、图论、随机控制和渐近标度律分析结合起来，揭示MR-MC网络优化与单无线电单通道（SR-SC）网络优化的基本因素，开发具有理论上可证明性能的低复杂度算法，并利用MR-MC网络在整合新技术以进一步增强容量方面的特殊优势。该项目将开发一套新颖的理论工具，从根本上解决多维资源空间中的容量优化和计算复杂性问题。在教育方面，这项跨学科研究不仅为本科生和研究生提供各种培训项目，还将激励学生（特别是博士生）以创新、开放和跨学科的视角追求高质量的研究。在业界，本课题所开发的容量规划和动态网络控制技术对实际无线网络设计具有重要意义，具有转化为低复杂度和可验证效率的实用网络协议的潜力。