Fractional Programming for Communication Systems-Part I: Power Control and Beamforming
Fractional Programming for Communication Systems-Part I: Power Control and Beamforming
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DOI：
10.1109/tsp.2018.2812733
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发表时间：
2018-05-15
影响因子：
5.4
通讯作者：
Yu, Wei
中科院分区：
文献类型：
--
作者：
Shen, Kaiming;Yu, Wei
分式规划（FP）是一类涉及比率项的优化问题。这篇由两部分组成的论文探讨了FP在通信系统设计和优化中的应用。本文的第一部分着重于FP理论和解决连续问题。主要的理论贡献是一种新的二次变换技术，用于解决多比凹凸FP问题，而传统的FP技术大多只能处理单比或最大-最小比情况。多比FP问题对于通信网络的优化非常重要，因为系统级设计通常涉及多个信噪比项。本文考虑了FP在通信系统设计中的应用，特别是在功率控制、波束形成和能效最大化方面的应用。这些应用实例表明，所提出的二次变换将原非凸问题转化为一系列凸问题，可以极大地促进涉及比率的优化。这种基于fp的问题重新表述产生了一种有效的迭代优化算法，该算法收敛于平稳点。本文进一步证明了所提出的FP方法与文献中其他知名算法（如不动点迭代和加权最小均方误差波束形成）之间的密切联系。本文第二部分讨论了离散问题的优化问题。
Fractional programming (FP) refers to a family of optimization problems that involve ratio term(s). This two-part paper explores the use of FP in the design and optimization of communication systems. Part I of this paper focuses on FP theory and on solving continuous problems. The main theoretical contribution is a novel quadratic transform technique for tackling the multiple-ratio concave-convex FP problem-in contrast to conventional FP techniques that mostly can only deal with the single-ratio or the max-min-ratio case. Multiple-ratio FP problems are important for the optimization of communication networks, because system-level design often involves multiple signal-to-interference-plus-noise ratio terms. This paper considers the applications of FP to solving continuous problems in communication system design, particularly for power control, beamforming, and energy efficiency maximization. These application cases illustrate that the proposed quadratic transform can greatly facilitate the optimization involving ratios by recasting the original nonconvex problem as a sequence of convex problems. This FP-based problem reformulation gives rise to an efficient iterative optimization algorithm with provable convergence to a stationary point. The paper further demonstrates close connections between the proposed FP approach and other well-known algorithms in the literature, such as the fixed-point iteration and the weighted minimum mean-square-error beamforming. The optimization of discrete problems is discussed in Part II of this paper.