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雷达卡
摘要翻译：
本文研究了具有低分辨率数模转换器的大规模多用户、多输入、多输出（MU-MIMO）系统中的预编码问题。在以前关于这个主题的文献中，通常假定信道状态信息(CSI)是完全已知的。然而，在实际应用中，CSI不可避免地受到噪声的污染。本文首次提出了一种改进粗量化MU-MIMO系统误码性能的特征推断(EI)预编码方案，该方案用两个矩形随机矩阵（RRMs）的和进行数学建模。该方法不是基于RRM进行分析，而是利用Girko的Hermitization技巧，通过将RRM扩展为块对称信道矩阵(BSCA)来利用块随机矩阵理论。特别地，我们推导了BSCA特征值的经验分布，并建立了真实BSCA与其噪声观测之间的极限谱分布联系。然后，基于这些理论结果，我们提出了一种基于EI的矩匹配方法用于CSI相关噪声水平估计和一种旋转不变估计方法用于CSI重建。在清理后的CSI基础上，利用Bussgang定理和Lagrangian乘子法解决了量化预编码问题。最后通过数值仿真验证了所提出的方法，结果证明了所提出的预编码器的有效性。
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英文标题：
《Eigen-Inference Precoding for Coarsely Quantized Massive MU-MIMO System
with Imperfect CSI》
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作者：
Lei Chu, Robert Qiu, Fei Wen
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最新提交年份：
2019
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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 work considers the precoding problem in massive multiuser multiple-input multiple-output (MU-MIMO) systems equipped with low-resolution digital-to-analog converters (DACs). In previous literature on this topic, it is commonly assumed that the channel state information (CSI) is perfectly known. However, in practical applications the CSI is inevitably contaminated by noise. In this paper, we propose, for the first time, an eigen-inference (EI) precoding scheme to improve the error performance of the coarsely quantized massive MU-MIMO systems under imperfect CSI, which is mathematically modeled by a sum of two rectangular random matrices (RRMs). Instead of performing analysis based on the RRM, using Girko's Hermitization trick, the proposed method leverages the block random matrix theory by augmenting the RRM into a block symmetric channel matrix (BSCA). Specially, we derive the empirical distribution of the eigenvalues of the BSCA and establish the limiting spectra distribution connection between the true BSCA and its noisy observation. Then, based on these theoretical results, we propose an EI-based moments matching method for CSI-related noise level estimation and a rotation invariant estimation method for CSI reconstruction. Based on the cleaned CSI, the quantized precoding problem is tackled via the Bussgang theorem and the Lagrangian multiplier method. The prosed methods are lastly verified by numerical simulations and the results demonstrate the effectiveness of the proposed precoder.
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PDF链接：
https://arxiv.org/pdf/1803.0023
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