用于Massive MIMO的有限大天线阵：特性与 系统影响

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摘要翻译：
Massive MIMO被认为是5G的关键技术。各种研究分析了天线数量的影响，仅依赖于信道特性，并假设在非常大的阵列中均匀的天线增益。本文研究了互耦和边缘效应对阵列增益方向图变化的影响。我们的分析集中在贴片天线与偶极子天线的比较上，偶极子天线是当今massive MIMO实验中典型使用的天线。通过仿真和测量，我们发现有限贴片阵列比偶极子阵列具有更小的增益方向图变化。大的增益方向图变化对massive MIMO系统的影响是，并非所有天线对所有用户的贡献相等，并且单个用户看到的有效天线数减少。结果表明，在系统级上，强迫零MIMO检测器对所有用户的影响是降低率，贴片阵列和偶极子阵列的降低率分别为20%和35%。而最大比例合并则引入了用户的不公平。
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英文标题：
《Finite Large Antenna Arrays for Massive MIMO: Characterization and
  System Impact》
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作者：
Cheng-Ming Chen, Vladimir Volski, Liesbet Van der Perre, Guy A. E.
  Vandenbosch and Sofie Pollin
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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：
  Massive MIMO is considered a key technology for 5G. Various studies analyze the impact of the number of antennas, relying on channel properties only and assuming uniform antenna gains in very large arrays. In this paper, we investigate the impact of mutual coupling and edge effects on the gain pattern variation in the array. Our analysis focuses on the comparison of patch antennas versus dipoles, representative for the antennas typically used in massive MIMO experiments today. Through simulations and measurements, we show that the finite patch array has a lower gain pattern variation compared with a dipole array. The impact of a large gain pattern variation on the massive MIMO system is that not all antennas contribute equally for all users, and the effective number of antennas seen for a single user is reduced. We show that the effect of this at system level is a decreased rate for all users for the zero-forcing MIMO detector, up to 20% for the patch array and 35% for the dipole array. The maximum ratio combining on the other hand, introduces user unfairness.
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PDF链接：
https://arxiv.org/pdf/1804.02072

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关键词：massive mass MIM mas IMO 天线 引入 show antennas 阵列