NEDG: Locally-optimal Power, Rate Adaptation and Scheduling in Wireless Networks

通信网络的发展极大地增加了信息的可用性，提高了生产力。继续从连接中获益的一个关键要求是普及无线网络的增长。无线网络资源有限，主要是由于其广播性质。缓解这种约束需要一个有效的、分布式的解决方案来解决媒体访问控制（MAC）问题。在这个项目中，研究者研究了“局部最优调度和物理层适应”，通过将物理层（PHY）模型纳入最大调度以提高准确性，以及优先级机制以提高效率。这将导致设计高效的分布式MAC协议，从PHY的角度来看，其保证是准确的。这项研究的结果通过适当的出版物和研讨会进行传播，以指导未来无线网络MAC协议的发展，并纳入大学？S本科和研究生课程。在一般的自组织无线网络中，物理层自适应和调度（MAC）是一个np难题，特别是在需要分布式解决方案的情况下。在这个项目中，研究人员研究局部最优算法，最大调度的推广，使用证明稳定性的广泛排队结果，并将其适应于无线网络，并仔细关注PHY模型。使用图论和优化理论分析对这些算法的性能进行理论表征，以及对最优（尽管是集中式）MAC算法设计的直觉。本研究的核心思想是通信理论、排队论、图论和优化的交叉点，利用李亚普诺夫函数和流体模型的概念，将其纳入大学课程。

The growth of communication networks has profoundly increased the availability of information and increased productivity. A key requirement in continuing to reap the benefits of connectivity is the growth of pervasive wireless networks. Wireless networks are resource constrained, primarily due to their broadcast nature. Alleviation of this constraint requires an efficient, distributed solution to the Medium Access Control (MAC) problem. In this project, the investigators study `locally-optimal scheduling and PHY adaptation', by incorporating physical layer (PHY) models into maximal scheduling for increased accuracy, and priority mechanisms for increased efficiency. This will result in the design of efficient, distributed MAC protocols, whose guarantees are accurate, from the PHY perspective. The results from this research, which is training a graduate Ph.D., are disseminated through appropriate publications and seminars, to guide the development of future wireless network MAC protocols and are incorporated into the university?s undergraduate and graduate courses.PHY adaptation and scheduling (MAC) in general ad hoc wireless networks, is an NP-hard problem, especially, if distributed solutions are desired. In this project, the investigators study locally-optimal algorithms, a generalization of maximal scheduling, using broad queuing results that prove stability, and adapt them to wireless networks, with careful attention paid to the PHY model. Theoretical characterization of the performance of these algorithms is carried out using graph theoretic and optimization theoretic analysis, along with intuition on the design of optimal (though, centralized) MAC algorithms. The key ideas of this research, which are at the intersection of communication theory, queuing theory, graph theory and optimization, are incorporated into university courses, using the concepts of Lyapunov functions and fluid models.