Machine learning inspired energy-efficient hybrid precoding for mmWave massive MIMO systems
Machine learning inspired energy-efficient hybrid precoding for mmWave massive MIMO systems
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DOI：
10.1109/icc.2017.7997065
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发表时间：
2017-05
期刊：
影响因子：
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通讯作者：
Xinyu Gao;L. Dai;Ying Sun;Shuangfeng Han;I. Chih-Lin
中科院分区：
文献类型：
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作者：
Xinyu Gao;L. Dai;Ying Sun;Shuangfeng Han;I. Chih-Lin
混合预编码是一种很有前途的毫米波大规模MIMO系统技术，因为它可以大大减少所需的射频（RF）链的数量，而不会造成明显的性能损失。然而，现有的混合预编码方案大多需要复杂的移相器网络，且能耗较高。在本文中，我们提出了一种节能的混合预编码架构，其中模拟部分由少量开关和逆变器而不是大量高分辨率移相器来实现。分析表明，当天线数趋于无穷大时，所提出的混合预编码结构与传统预编码结构的性能差距很小，且保持不变。然后，受机器学习中发展的交叉熵（CE）优化的启发，我们提出了一种基于自适应交叉熵（ACE）的混合预编码方案。其目的是通过最小化CE，自适应地更新混合预编码器中元素的概率分布，从而以足够高的概率生成接近最优解的解。仿真结果表明，该方案可以达到接近最优的和速率性能，并且比传统方案具有更高的能效。
Hybrid precoding is a promising technique for mmWave massive MIMO systems, as it can considerably reduce the number of required radio-frequency (RF) chains without obvious performance loss. However, most of the existing hybrid precoding schemes require a complicated phase shifter network, which still involves high energy consumption. In this paper, we propose an energy-efficient hybrid precoding architecture, where the analog part is realized by a small number of switches and inverters instead of a large number of high-resolution phase shifters. Our analysis proves that the performance gap between the proposed hybrid precoding architecture and the traditional one is small and keeps constant when the number of antennas goes to infinity. Then, inspired by the cross-entropy (CE) optimization developed in machine learning, we propose an adaptive CE (ACE)-based hybrid precoding scheme for this new architecture. It aims to adaptively update the probability distributions of the elements in hybrid precoder by minimizing the CE, which can generate a solution close to the optimal one with a sufficiently high probability. Simulation results verify that our scheme can achieve the near-optimal sum-rate performance and much higher energy efficiency than traditional schemes.