Massive MIMO中利用定时偏移和过量的随机接入 天线

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摘要翻译：
Massive MIMO系统是处理快速增长的数据流量的一种很有吸引力的方式，它的基站配备了数百个天线。随着用户设备数量的增加，在现代网络中，初始接入和切换将被用户冲突淹没。本文提出了一种简单地利用Massive MIMO网络中设想的大量天线来解决冲突和进行定时估计的随机接入协议。进入网络的UE在时域和频域进行扩展，在基站使用子空间分解方法以封闭形式估计它们的定时偏移。该信息用于计算信道估计，该信道估计随后由基站用于与检测到的UE通信。Massive MIMO良好的传播条件抑制了UE间的干扰，而固有的定时不对齐提高了协议的检测能力。数值结果验证了该方法在非相关衰落信道和相关衰落信道下的性能。在$2.5×10^3$UE的情况下（在给定的随机接入块中），可以以1%的概率同时激活，并且总共有$16$的频率-时间码，结果表明，在$100$天线的情况下，所提出的过程以75%的概率成功地检测给定的UE，同时提供可靠的定时估计。
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英文标题：
《Random Access in Massive MIMO by Exploiting Timing Offsets and Excess
  Antennas》
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作者：
Luca Sanguinetti, Antonio A. D'Amico, Michele Morelli, Merouane Debbah
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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：
  Massive MIMO systems, where base stations are equipped with hundreds of antennas, are an attractive way to handle the rapid growth of data traffic. As the number of user equipments (UEs) increases, the initial access and handover in contemporary networks will be flooded by user collisions. In this paper, a random access protocol is proposed that resolves collisions and performs timing estimation by simply utilizing the large number of antennas envisioned in Massive MIMO networks. UEs entering the network perform spreading in both time and frequency domains, and their timing offsets are estimated at the base station in closed-form using a subspace decomposition approach. This information is used to compute channel estimates that are subsequently employed by the base station to communicate with the detected UEs. The favorable propagation conditions of Massive MIMO suppress interference among UEs whereas the inherent timing misalignments improve the detection capabilities of the protocol. Numerical results are used to validate the performance of the proposed procedure in cellular networks under uncorrelated and correlated fading channels. With $2.5\times10^3$ UEs that may simultaneously become active with probability 1\% and a total of $16$ frequency-time codes (in a given random access block), it turns out that, with $100$ antennas, the proposed procedure successfully detects a given UE with probability 75\% while providing reliable timing estimates.
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PDF链接：
https://arxiv.org/pdf/1711.0683

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关键词：massive mass IMO mas MIM 网络 冲突 用于 Massive 方法