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雷达卡
摘要翻译：
本文研究了大规模多输入多输出(MIMO)系统中联合下行链路信道估计和用户分组问题，其动机是利用邻近用户之间额外的公共稀疏性可以提高信道估计性能。在文献中，一种常用的组稀疏模型假设每个组中的用户共享一个统一的稀疏模式。然而，在实践中，这种过于简化的假设通常是不成立的，即使对于物理上接近的用户也是如此。各组中偏离统一稀疏模式的离群值可能会显著降低普通稀疏模式的有效性，从而给信道估计带来有限（或负）增益。为了在实践中更好地捕捉群体稀疏结构，我们提供了一个包含两个稀疏成分的通用模型：公共共享稀疏和个体稀疏，其中额外的个体稀疏解释了任何离群点。然后，我们提出了一种新的基于稀疏贝叶斯学习(SBL)的框架来解决一般稀疏模型下的联合信道估计和用户分组问题。该框架能够充分利用邻近用户之间的稀疏性，同时排除离群点的有害影响。仿真结果表明，与现有的最先进的基线相比，性能有了很大的提高。
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英文标题：
《Joint Channel Estimation and User Grouping for Massive MIMO Systems》
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作者：
Jisheng Dai, An Liu, and Vincent K. N. Lau
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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 addresses the problem of joint downlink channel estimation and user grouping in massive multiple-input multiple-output (MIMO) systems, where the motivation comes from the fact that the channel estimation performance can be improved if we exploit additional common sparsity among nearby users. In the literature, a commonly used group sparsity model assumes that users in each group share a uniform sparsity pattern. In practice, however, this oversimplified assumption usually fails to hold, even for physically close users. Outliers deviated from the uniform sparsity pattern in each group may significantly degrade the effectiveness of common sparsity, and hence bring limited (or negative) gain for channel estimation. To better capture the group sparse structure in practice, we provide a general model having two sparsity components: commonly shared sparsity and individual sparsity, where the additional individual sparsity accounts for any outliers. Then, we propose a novel sparse Bayesian learning (SBL)-based framework to address the joint channel estimation and user grouping problem under the general sparsity model. The framework can fully exploit the common sparsity among nearby users and exclude the harmful effect from outliers simultaneously. Simulation results reveal substantial performance gains over the existing state-of-the-art baselines.
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PDF链接：
https://arxiv.org/pdf/1804.09295
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