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雷达卡
摘要翻译：
Massive MIMO是多用户MIMO的一种变体，其中基站(BS)$M$的天线数目非常大，并且通常比服务的用户数（数据流）$K$大得多。最近的研究已经说明了这种系统的系统级优势，特别是增加天线数目的好处。然而，这些好处是以硬件和计算复杂性的急剧增加为代价的。这部分是由于这样的事实，即BS需要计算合适的波束成形矢量以便相干地向每个用户发送/接收数据，其中所产生的复杂度与天线的数量$m$和所服务的用户的数量$k$成比例地增长。近年来，基于随机矩阵理论的工具，在$M，K\to\infty$和$\frac{K}{M}\to\rho\in(0,1)$渐近域内提出了不同的算法来降低这种复杂性。然而，所有这些技术的基本假设是，用户信道向量的精确统计量（协方差矩阵）是先验已知的。这是远远不现实的，尤其是在$m到infty的高模糊状态下，估计潜在的协方差矩阵是众所周知的一个非常具有挑战性的问题。本文提出了一种在massive MIMO系统中设计波束形成矢量的新技术。该方法基于随机化Kaczmarz算法，不需要用户信道向量的统计知识。我们从理论上分析了该算法的性能，并通过数值仿真将其与其他竞争技术的性能进行了比较。我们的结果表明，我们提出的技术具有相当的性能，而不需要知道用户信道向量的统计信息。
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英文标题：
《Low-Complexity Statistically Robust Precoder/Detector Computation for
Massive MIMO Systems》
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作者：
Mahdi Nouri Boroujerdi, Saeid Haghighatshoar, Giuseppe Caire
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最新提交年份：
2018
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分类信息：
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：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：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 is a variant of multiuser MIMO in which the number of antennas at the base station (BS) $M$ is very large and typically much larger than the number of served users (data streams) $K$. Recent research has illustrated the system-level advantages of such a system and in particular the beneficial effect of increasing the number of antennas $M$. These benefits, however, come at the cost of dramatic increase in hardware and computational complexity. This is partly due to the fact that the BS needs to compute suitable beamforming vectors in order to coherently transmit/receive data to/from each user, where the resulting complexity grows proportionally to the number of antennas $M$ and the number of served users $K$. Recently, different algorithms based on tools from random matrix theory in the asymptotic regime of $M,K \to \infty$ with $\frac{K}{M} \to \rho \in (0,1)$ have been proposed to reduce such complexity. The underlying assumption in all these techniques, however, is that the exact statistics (covariance matrix) of the channel vectors of the users is a priori known. This is far from being realistic, especially that in the high-dim regime of $M\to \infty$, estimation of the underlying covariance matrices is well known to be a very challenging problem. In this paper, we propose a novel technique for designing beamforming vectors in a massive MIMO system. Our method is based on the randomized Kaczmarz algorithm and does not require knowledge of the statistics of the users channel vectors. We analyze the performance of our proposed algorithm theoretically and compare its performance with that of other competitive techniques via numerical simulations. Our results indicate that our proposed technique has a comparable performance while it does not require the knowledge of the statistics of the users channel vectors.
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PDF链接：
https://arxiv.org/pdf/1711.11405
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