摘要翻译：
对于大规模的多输入多输出(MIMO)系统，线性最小均方误差(MMSE)检测已经被证明可以获得接近最优的性能，但由于大规模的矩阵求逆而导致复杂度过高。基于Gauss-Seidel(GS)方法的检测算法无需矩阵求逆，比传统的基于Neumann级数展开(NSE)的检测算法更有效。提出了一种高效的基于GS的massive MIMO软输出数据检测器，并给出了相应的VLSI结构。为了加快GS方法的收敛速度，提出了一种新的初始解。在VLSI体系结构层面提出了几个优化方案，以进一步减少处理延迟和面积。在Xilinx Virtex-7XC7VX690T FPGA上，针对128基站天线和8用户massive MIMO系统的参考实现结果表明，基于GS的数据检测器的吞吐量为732Mb/s，误码率性能接近MMSE。我们的实现结果表明，所提出的解决方案在复杂度和效率方面优于现有的设计，特别是在具有挑战性的传播条件下。
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英文标题：
《Efficient Soft-Output Gauss-Seidel Data Detector for Massive MIMO
  Systems》
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作者：
Chuan Zhang (1 and 2 and 3), Zhizhen Wu (1 and 2 and 3), Christoph
  Studer (4), Zaichen Zhang (2 and 3), Xiaohu You (3) ((1) Lab of Efficient
  Architectures for Digital-communication and Signal-processing (LEADS), (2)
  Quantum Information Center, Southeast University, China, (3) National Mobile
  Communications Research Laboratory, (4) School of Electrical and Computer
  Engineering, Cornell University, USA)
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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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一级分类：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
  For massive multiple-input multiple-output (MIMO) systems, linear minimum mean-square error (MMSE) detection has been shown to achieve near-optimal performance but suffers from excessively high complexity due to the large-scale matrix inversion. Being matrix inversion free, detection algorithms based on the Gauss-Seidel (GS) method have been proved more efficient than conventional Neumann series expansion (NSE) based ones. In this paper, an efficient GS-based soft-output data detector for massive MIMO and a corresponding VLSI architecture are proposed. To accelerate the convergence of the GS method, a new initial solution is proposed. Several optimizations on the VLSI architecture level are proposed to further reduce the processing latency and area. Our reference implementation results on a Xilinx Virtex-7 XC7VX690T FPGA for a 128 base-station antenna and 8 user massive MIMO system show that our GS-based data detector achieves a throughput of 732 Mb/s with close-to-MMSE error-rate performance. Our implementation results demonstrate that the proposed solution has advantages over existing designs in terms of complexity and efficiency, especially under challenging propagation conditions.
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PDF链接：
https://arxiv.org/pdf/1804.06737