摘要翻译：
本工作集中于多用户MIMO系统的下行链路通信，其中基站天线和用户的接收天线都是有效的，但是在每次传输中，基站只选择接收天线的子集来接收信息符号，并且特定选择的子集（模式）表示传送给用户的部分信息。本文给出了该系统的数学模型，并给出了对该系统进行分析的通用表达式。基于这些表达式，我们提出了一个过程，以最大的检测信噪比为目标，通过ERB优化选择用于不同用户传输的天线方向图集。性能结果，有和没有优化程序，给出了不同的场景。
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英文标题：
《Precoding and Spatial Modulation in the Downlink of MU-MIMO Systems》
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作者：
Azucena Duarte and Raimundo Sampaio-Neto
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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：
  This work focuses on the downlink communication of a multiuser MIMO system where the base station antennas and the users' receiving antennas are all active, but at each transmission, only a subset of the receive antennas is selected by the base station to receive the information symbols, and the particular chosen subset (pattern) represents part of the information conveyed to the user. In this paper we present a mathematical model for the system and develop expressions that are fairly general and adequate for its analysis. Based on these expressions we propose a procedure to optimize the choice by the ERB of the sets of antenna patterns to be used in the transmissions to the different users, aiming at the maximization of the detection signal-to-noise ratio. Performance results, with and without the optimization procedure, are presented for different scenarios.
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PDF链接：
https://arxiv.org/pdf/1803.01895