A Fundamental Study on Energy Efficient Wireless Communication Networks: Modeling, Algorithms, and Applications

无线网络的快速发展和广泛部署导致了能源需求的快速增长，这迫切需要节能的网络技术。与此同时，无线网络正在演变成具有多维资源（包括通信链路、无线电、信道、天线和发射功率）的复杂形式，因此高度要求用于能量效率优化的低复杂度算法。该项目旨在通过建立统一的分析框架，开发高效和低复杂度的算法，并将通用研究应用于第五代（5G）蜂窝系统的重要场景，对节能无线网络进行基础研究。这种跨学科的研究不仅将提供各种培训项目，以本科和研究生的学习，但也激励学生追求高质量的研究与创造性，开放的思想，跨学科的角度。该项目将证明，一个统一的多维优化框架的能源效率优化可以构建的原则，调度适当的传输模式，这是由网络的干扰模型定义。利用这种统一的优化模型，低复杂度分解技术基本上与根据信号与干扰加噪声比的物理干扰模型下的最大加权传输模式（MWTP）问题相关。MWTP问题的近似算法和相关的性能分析（这是NP-难的一般）是关键的研究问题进行研究。解决能源效率优化问题的分布式算法进一步涉及多目标优化分解，以及创新的李雅普诺夫函数设计和物理干扰模型下的相关稳定性分析，这些也将在本项目中讨论。通过统一多维框架中的创新建模和算法，将在几个重要的5G蜂窝场景中实现节能解决方案，包括在网络层将大规模MIMO干扰缓解与流量约束结合起来的联合优化以及在系统级的基站睡眠，大规模MIMO干扰模型下MWTP问题的公式化和算法开发，以及对大规模MIMO和设备到设备通信之间的相互作用进行建模。在这个项目中，所提出的研究无缝地集成了优化，图论，对偶分解，近似算法，无线通信和网络等领域的研究。 研究成果有望为5G蜂窝系统的发展提供重要指导。

The rapid development and wide deployment of wireless networks incur a fast escalation of energy demand, which eagerly calls for energy-efficient networking techniques. At the same time, wireless networks are evolving into complex forms with multi-dimensional resources including communication link, radio, channel, antenna, and transmit power; algorithms with low complexity for energy efficiency optimization are highly demanded. This project targets at a fundamental study on energy-efficient wireless networking through establishment of a uniformed analytical framework, development of efficient and low-complexity algorithms, and application of the generic studies into important scenarios in the fifth generation (5G) cellular systems. This interdisciplinary research will not only provide various training projects to undergraduate and graduate studies, but also inspire students to pursue high-quality research with a creative, open-minded, and cross-disciplinary perspective. This project is going to demonstrate that a uniformed multidimensional optimization framework for energy efficiency optimization can be constructed by the principle of scheduling proper transmission patterns, which are defined by the interference model of the network. With such a uniformed optimization model, low-complexity decomposition techniques are fundamentally related to a maximum weighted transmission pattern (MWTP) problem, under a physical interference model according to the signal-to-interference-plus-noise ratio. Approximation algorithms and associated performance analysis for the MWTP problem (which is NP-hard in general) are critical research issues to be studied. Distributed algorithms for solving the energy efficiency optimization problem further involves decomposition with multi-objective optimization, and innovative Lyapunov function design and associated stability analysis under the physical interference model, which will also be addressed in this project. Energy efficient solutions in a couple of important 5G cellular scenarios will be enabled through innovative modeling and algorithms in the uniformed multidimensional framework, including joint optimization that incorporates massive MIMO interference mitigation with flow constraint at network layer and base station sleeping at system level, formulation and algorithm development for a MWTP problem under the massive MIMO interference model, and modeling of the interplay between massive MIMO and device-to-device communications. In this project, the proposed research seamlessly integrates studies in the areas of optimization, graph theory, dual decomposition, approximation algorithms, and wireless communication and networking. The research outcomes are expected to provide important guidance for the development of the 5G cellular systems.