摘要翻译：
最新的第五代无线通信物理层标准提出了快速原型平台的必要性。为此，我们开发了基于多核通用处理器的大规模多输入多输出(MIMO)原型平台RaPro。为了提高RaPro性能，需要高性能的检测和波束形成，而这两者都需要准确的信道状态信息(CSI)。本文采用了基于线性最小均方误差(LMMSE)的信道估计器，并将其封装在RaPro中，以获得更精确的CSI。考虑到massive MIMO信道统计量的高复杂性和未知性，设计了低复杂度的LMMSE信道估计器，以缓解随着天线数目的增加而增加的复杂度，并为massive MIMO上行检测和下行波束形成预留更多的计算资源。仿真结果表明，所设计的低复杂度方法具有较高的均方误差性能和鲁棒性。室内和走廊场景测试表明，在误码率性能方面有显著提高。时间开销分析证明了所实现的上行接收机在RAPRO上的实用性和实时传输能力。
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英文标题：
《Implementation of Massive MIMO Uplink Receiver on RaPro Prototyping
  Platform》
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作者：
Xuanxuan Gao, Zhichao Huang, Xintong Lu, Senjie Zhang, Chao-Kai Wen
  and Shi Jin
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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：
  The updated physical layer standard of the fifth generation wireless communication suggests the necessity of a rapid prototyping platform. To this end, we develop RaPro, a multi-core general purpose processor-based massive multiple-input-multiple-output (MIMO) prototyping platform. To enhance RaPro, high performance detection and beamforming are needed, whereas both of them request for accurate channel state information (CSI). In this paper, linear minimum mean square error (LMMSE)-based channel estimator is adopted and encapsulated inside RaPro to gain more accurate CSI. Considering the high comlexity and unknown of channel statistics, we design low-complexity LMMSE channel estimator to alleviate the rising complexity along with increasing antenna number and set more computational resource aside for massive MIMO uplink detection and downlink beamforming. Simulation results indicate the high mean square error performance and robustness of designed low-complexity method. Indoor and corridor scenario tests show prominent improvement in bit error rate performance. Time cost analysis proves the practical use and real-time transmission ability of the implemented uplink receiver on RaPro.
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PDF链接：
https://arxiv.org/pdf/1802.05427