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带来了能源需求的快速升级，迫切需要高能效的网络技术。与此同时，无线网络正在向复杂的形式演变，拥有包括通信链路、无线电、信道、天线和发射功率在内的多维资源，对低复杂度的能量效率优化算法提出了很高的要求。该项目旨在通过建立统一的分析框架、开发高效和低复杂性的算法以及将通用研究应用于第五代(5G)蜂窝系统中的重要场景来对节能无线网络进行基础研究。这种跨学科研究不仅将为本科生和研究生提供各种培训项目，还将激励学生以创造性的、开放的和跨学科的视角追求高质量的研究。该项目将证明，通过调度由网络干扰模型定义的适当的传输模式的原则，可以构建统一的多维优化框架来进行能量效率优化。利用这种统一的优化模型，在根据信干噪比的物理干扰模型下，低复杂度分解技术基本上与最大加权传输模式(MWTP)问题相关。MWTP问题(一般是NP-Hard问题)的近似算法和相关性能分析是需要研究的关键研究问题。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，以及物理干扰模型下创新的Lyapunov函数设计和相关稳定性分析，这也将在本项目中讨论。通过统一的多维框架中的创新建模和算法，将能够在几个重要的5G蜂窝场景中实现高能效解决方案，包括将大规模MIMO干扰缓解与网络层和系统级睡眠的基站的流量约束相结合的联合优化，大规模MIMO干扰模型下MWTP问题的公式和算法开发，以及大规模MIMO和设备到设备通信之间的相互作用的建模。在这个项目中，拟议的研究无缝地整合了优化、图论、对偶分解、近似算法以及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.