恒包络和迫零预编码器的性能比较 在多用户Massive MIMO中

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摘要翻译：
本文研究了基于MIMO的大规模多用户蜂窝网络中一种恒定包络(CE)型空间预编码器的采用及其性能。我们首先提出了一种高效的计算方法来获得这种CE预编码器的天线样本。然后，我们评估了基于CE预编码器的多用户下行链路(DL)系统的性能，并将其与更普通的迫零(ZF)空间预编码器的相应性能进行了比较。我们还特别分析了现实的高度非线性功率放大器(PAs)如何影响可实现的DL性能，因为在大阵列或大规模MIMO系统中，单个PA单元被期望是小的、便宜的和接近饱和的，以提高能量效率。结果表明，与传统的基于ZF预编码器的系统相比，在基于CE预编码器的系统中PA输入信号的峰均功率比(PAPR)大大降低，使得PA单元更接近饱和，同时允许在预定接收机处达到更高的信干噪比(SINRs)。结果表明，当PA单元被推到饱和区时，CE预编码器的SINRs比ZF预编码器的SINRs高5-6 dBs。当寻求改善移动蜂窝网络的频谱和能量效率时，如此大的收益是一个实质性的好处。
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英文标题：
《Performance Comparison of Constant Envelope and Zero-forcing Precoders
  in Multiuser Massive MIMO》
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作者：
Alberto Brihuega, Lauri Anttila and Mikko Valkama
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  In this article, the adoption and performance of a constant envelope (CE) type spatial precoder is addressed in large-scale multiuser MIMO based cellular network. We first formulate an efficient computing solution to obtain the antenna samples of such CE precoder. We then evaluate the achievable CE precoder based multiuser downlink (DL) system performance and compare it with the corresponding performance of more ordinary zero-forcing (ZF) spatial precoder. We specifically also analyze how realistic highly nonlinear power amplifiers (PAs) affect the achievable DL performance, as the individual PA units in large-array or massive MIMO systems are expected to be small, cheap and operating close to saturation for increased energy-efficiency purposes. It is shown that the largely reduced peak-to-average power ratio (PAPR) of the PA input signals in the CE precoder based system allows for pushing the PA units harsher towards saturation, while allowing to reach higher signal-to-interference-plus-noise ratio (SINRs) at the intended receivers compared to the classical ZF precoder based system. The obtained results indicate that the CE precoder can outperform the ZF precoder by up to 5-6 dBs, in terms of the achievable SINRs, when the PA units are pushed towards their saturating region. Such large gains are a substantial benefit when seeking to improve the spectral and energy-efficiencies of the mobile cellular networks.
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PDF链接：
https://arxiv.org/pdf/1804.02224

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关键词：massive mass MIM mas IMO SINRs 允许 based 使得 基于