摘要翻译：
高功耗和硬件成本是实现大规模多输入多输出(mMIMO)系统的两大障碍。一个有希望的解决方案是采用低分辨率的模数转换器。在本文中，我们考虑了一个具有多电平混合ADC结构的通用mMIMO多路中继系统，其中每个天线连接到一对任意分辨率的ADC。利用Bussgang分解定理和Lloyd-Max量化算法，分别考虑完全信道状态信息和不完全信道状态信息，导出了迫零中继平均可达速率的紧闭式近似。为了克服多路中继、复杂的ZF波束形成矩阵和一般混合ADC结构带来的挑战，我们提出了一种利用高斯矩阵奇异值分解(SVD)、高斯矩阵奇异值分布和Haar矩阵性质进行可达速率分析的新方法。结果明确地显示了用户和中继发射功率以及中继天线和用户数对可实现速率行为的影响。最重要的是，它量化了低分辨率ADC和信道估计误差造成的性能下降。我们证明了平均可达速率与ADC分辨率分布的量化系数平均值的平方几乎成线性关系。
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英文标题：
《Performance Analysis of Massive MIMO Multi-Way Relay Networks with
  Low-Resolution ADCs》
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作者：
Samira Rahimian, Yindi Jing, Masoud Ardakani
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最新提交年份：
2019
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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：
  High power consumption and hardware cost are two barriers for practical massive multiple-input multiple-output (mMIMO) systems. A promising solution is to employ low-resolution analog-to-digital converters (ADCs). In this paper, we consider a general mMIMO multi-way relaying system with a multi-level mixed-ADC architecture, in which each antenna is connected to an ADC pair of an arbitrary resolution. By leveraging on Bussgang's decomposition theorem and Lloyd-Max algorithm for quantization, tight closed-form approximations are derived for the average achievable rates of zero-forcing (ZF) relaying considering both perfect and imperfect channel state information (CSI). To conquer the challenges caused by multi-way relaying, the complicated ZF beam-forming matrix, and the general mixed-ADC structure, we develop a novel method for the achievable rate analysis using the singular-value decomposition (SVD) for Gaussian matrices, distributions of the singular values of Gaussian matrices, and properties of Haar matrices. The results explicitly show the achievable rate behavior in terms of the user and relay transmit powers and the numbers of relay antennas and users. Most importantly, it quantifies the performance degradation caused by low-resolution ADCs and channel estimation error. We demonstrate that the average achievable rate has an almost linear relation with the square of the average of quantization coefficients pertaining to the ADC resolution profile.
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PDF链接：
https://arxiv.org/pdf/1804.07254