Energy and spectral efficiency tradeoff in massive MIMO systems with multi-objective adaptive genetic algorithm
Energy and spectral efficiency tradeoff in massive MIMO systems with multi-objective adaptive genetic algorithm
复制标题
使用多目标自适应遗传算法在大规模 MIMO 系统中进行能量和频谱效率权衡
DOI：
10.1007/s00500-018-3356-x
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发表时间：
2018-07
期刊：
影响因子：
4.1
通讯作者：
W. T. Song
中科院分区：
文献类型：
--
作者：
Y. Q. Hei;C. Zhan;W. T. Song
频谱效率（SE）和能量效率（EE）是大规模多输入多输出（MIMO）系统中的重要指标。然而，最大化EE和SE总是相互冲突的，它们很难同时实现。在本文中，我们专注于在多用户大规模MIMO系统中的发射天线的数量和发射功率的EE和SE之间的权衡优化。与以往的面向EE或面向SE的方法不同，EE-SE权衡问题被转化为一个多目标优化问题。为了有效地获得EE-SE折衷的Pareto最优前沿（POF），在非支配排序遗传算法（NSGA-II）的启发下，提出了一种多目标自适应遗传算法，以提高收敛速度.与几种著名的多目标算法的实验比较表明，该算法能够快速适应EE-SE权衡的真实POF，并在基准函数上保持良好的性能。
Spectral efficiency (SE) and energy efficiency (EE) are both important metrics in massive multiple-input multiple-output (MIMO) systems. However, maximizing EE and SE is always conflicting with each other, and they can hardly be achieved simultaneously. In this paper, we focus on the tradeoff optimization between EE and SE in multiuser massive MIMO systems in terms of the number of transmit antennas and the transmit power. Different from the previous EE-oriented or SE-oriented method, the EE–SE tradeoff problem is formulated into a multi-objective optimization problem. To efficiently attain the Pareto optimal front (POF) of EE–SE tradeoff, a multi-objective adaptive genetic algorithm, inspired by the non-dominated sorting genetic algorithm (NSGA-II), is proposed to improve the convergence speed. Experimental comparisons against several well-known multi-objective algorithms show that the proposed algorithm can quickly adapt to the true POF of EE–SE tradeoff and maintain good performance on benchmark functions in terms of the adopted performance metrics.
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