摘要翻译：
本文提出了一种用于光无线通信的波束域大规模多输入多输出(MIMO)传输技术。配备有大量光发射机的光基站通过发射透镜与多个用户终端通信。本文以LED发射器为研究对象，分析了透镜的光折射特性，建立了massive MIMO光传输信道模型。对于大量的LED，不同UT的信道矢量变得渐近正交。研究了光massive MIMO系统中的最大比传输和正则化迫零预编码，并提出了一种线性预编码设计，以最大化和速率。当发射机数目渐近增长时，我们进一步设计了预编码，并证明了波束分多址(BDMA)传输在和速率最大化时达到渐近最优性能。与没有发射透镜的光学MIMO不同，BDMA可以在总功率和每个发射机功率的限制下分别将和速率按比例提高到2k$和k$,其中k$是UT的数量。在非渐近情况下，我们证明了波束域最优功率分配的正交性条件，并提出了有效的波束分配算法。数值结果证实了我们提出的光束域光massive MIMO通信方法的显着改善的性能。
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英文标题：
《Beam Domain Massive MIMO for Optical Wireless Communications with
  Transmit Lens》
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作者：
Chen Sun, Xiqi Gao, Jiaheng Wang, Zhi Ding, and Xiang-Gen Xia
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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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英文摘要：
  This paper presents a novel massive multiple-input multiple-output (MIMO) transmission in beam domain for optical wireless communications. The optical base station equipped with massive optical transmitters communicates with a number of user terminals (UTs) through a transmit lens. Focusing on LED transmitters, we analyze light refraction of the lens and establish a channel model for optical massive MIMO transmissions. For a large number of LEDs, channel vectors of different UTs become asymptotically orthogonal. We investigate the maximum ratio transmission and regularized zero-forcing precoding in the optical massive MIMO system, and propose a linear precoding design to maximize the sum rate. We further design the precoding when the number of transmitters grows asymptotically large, and show that beam division multiple access (BDMA) transmission achieves the asymptotically optimal performance for sum rate maximization. Unlike optical MIMO without a transmit lens, BDMA can increase the sum rate proportionally to $2K$ and $K$ under the total and per transmitter power constraints, respectively, where $K$ is the number of UTs. In the non-asymptotic case, we prove the orthogonality conditions of the optimal power allocation in beam domain and propose efficient beam allocation algorithms. Numerical results confirm the significantly improved performance of our proposed beam domain optical massive MIMO communication approaches.
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PDF链接：
https://arxiv.org/pdf/1710.05282