Energy Efficiency Optimization for MIMO Distributed Antenna Systems With Pilot Contamination
Energy Efficiency Optimization for MIMO Distributed Antenna Systems With Pilot Contamination
复制标题
具有导频污染的 MIMO 分布式天线系统的能效优化
DOI：
10.1109/access.2018.2831210
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发表时间：
2018
期刊：
影响因子：
3.9
通讯作者：
You Xiaohu
中科院分区：
文献类型：
--
作者：
Xu Jun;Zhu Pengcheng;Li Jiamin;You Xiaohu
研究了带导频污染的多输入多输出分布式天线系统的能量效率最大化问题。与每个用户的服务质量约束和每个远程天线单元（RAU）的功率要求，我们制定的EE最大化问题作为一个联合优化稀疏发射波束成形，RAU选择，和RAU集群。所考虑的问题是一个非凸的多元优化问题。为了解决这个问题，我们将其转化为一个等价的参数规划问题（PPP）与给定的EE参数，并设计了一个两层优化方案来解决原问题。外层包含两种算法，分别基于Dinkelbach算法和二分搜索迭代更新EE参数。更具挑战性的问题在于内部循环，其中需要处理非凸多变量PPP。采用一系列技术，包括重加权$\ell_\mathbf {1}$ -范数、DC函数和半定松弛（SDR），将非凸多元PPP问题近似为凸SDR问题。此外，提出了一种启发式算法，以减少一个两层计划的复杂性。仿真结果表明，所提出的算法显着提高EE，并证明RAU选择和RAU聚类有助于更高的EE。
In this paper, we study the energy-efficiency (EE) maximization problem for a multiple-input multiple-output distributed antenna system (DAS) with pilot contamination. With per-user quality of service constraints and per remote antenna unit (RAU) power requirements, we formulate the EE maximization problem as a joint optimization of sparse transmit beamforming, RAU selection, and RAU clustering. The considered problem is a non-convex multivariate optimization problem. To solve the problem, we transform it to an equivalent parametric programming problem (PPP) with a given EE parameter and design a two-layer optimization scheme to solve the original problem. The outer layer involves two kinds of algorithms to iteratively update the EE parameter based on Dinkelbach’s algorithm and bi-section search, respectively. The more challenging issue lies in the inner loop, where a non-convex multivariate PPP needs to be tackled. A series of techniques, including the reweighted $\ell_\mathbf {1}$ -norm, D.C. function, and semidefinite relaxation (SDR), is adopted to approximate the non-convex multivariate PPP with a convex SDR problem. Furthermore, a heuristic algorithm is proposed to reduce the complexity of a two-layer scheme. Simulation results show that the proposed algorithms significantly improve the EE and demonstrate that RAU selection and RAU clustering contribute to a higher EE.
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影响因子：
16.4
作者：
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影响因子：
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作者：
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影响因子：
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作者：
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DOI：
10.1109/glocom.2011.6133999
发表时间：
2011-12
期刊：
2011 IEEE Global Telecommunications Conference - GLOBECOM 2011
影响因子：
--
作者：
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通讯作者：
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