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首页> 外文期刊>IEEE transactions on wireless communications >Decentralized WSEE Optimization for Massive MIMO Two-Way Half-Duplex AF Relaying
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Decentralized WSEE Optimization for Massive MIMO Two-Way Half-Duplex AF Relaying

机译:大规模MIMO两路半双工AF中继的分散式WSEE优化

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摘要

Design of energy-efficient wireless systems has recently attracted attention to reduce their carbon footprint. This paper optimizes non-convex weighted sum energy efficiency (WSEE) of a multi-pair two-way amplify-and-forward half-duplex massive multiple-input multiple output relay system. We optimize it by developing a two-layer decentralized successive convex approximation optimization framework. The first layer approximates the non-convex WSEE either as a generic convex program (GCP) or as a second order cone program (SOCP). The second layer decentrally solves the approximated problem using alternating direction method of multipliers. We show that the proposed iterative algorithm yields a Karush-Kuhn-Tucker point of the original WSEE problem. We numerically analyze the effect of weights on the energy efficiency (EE) of individual users, and show that the proposed framework enable us to meet the heterogeneous EE requirements. We also analytically and numerically show that the decentralized algorithm has lesser complexity than its centralized counterpart, but yields the same WSEE.
机译:节能无线系统的设计最近引起了人们的注意,以减少其碳足迹。本文优化了多对两路放大转发半双工大规模多输入多输出继电器系统的非凸加权和能效(WSEE)。我们通过开发两层分散的连续凸逼近优化框架来对其进行优化。第一层将非凸WSEE近似为通用凸规划(GCP)或二阶锥规划(SOCP)。第二层使用乘法器的交替方向方法分散地解决了近似问题。我们表明,提出的迭代算法产生了原始WSEE问题的Karush-Kuhn-Tucker点。我们通过数值分析权重对单个用户的能源效率(EE)的影响,并表明所提出的框架使我们能够满足异构EE的要求。我们还通过分析和数值分析表明,分散算法的复杂度低于集中算法,但产生的WSEE相同。

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