摘要翻译：
毫米波是下一代移动通信的一种很有前途的技术。大天线阵能够提供足够的预编码增益来克服毫米波频段的高路径损耗。然而，准确的信道状态信息是预编码设计的关键。不幸的是，信道使用开销和复杂度是高维数组信道估计的两大挑战。在本文中，我们提出了一个两阶段的方法，减少了信道使用开销和计算复杂度。具体来说，在第一阶段，我们估计信道矩阵的列子空间。在估计出的列子空间的基础上，设计训练发声器，获取列子空间的剩余系数矩阵。通过将估计任务分为两个阶段，第二阶段的训练测深器只针对列子空间，节省了信道占用和计算复杂度。
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英文标题：
《Two-stage Method for Millimeter Wave Channel Estimation》
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作者：
Wei Zhang, Shu-Hung Leung, and Taejoon Kim
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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：
  The millimeter wave is a promising technique for the next generation of mobile communication. The large antenna array is able to provide sufficient precoding gain to overcome the high pathloss at millimeter wave band. However, the accurate channel state information is the key for the precoding design. Unfortunately, the channel use overhead and complexity are two major challenges when estimating the channel with high-dimensional array. In this paper, we propose a two-stage approach which reduces the channel use overhead and the computational complexity. Specifically, in the first stage, we estimate the column subspace of the channel matrix. Based on the estimated column subspace, we design the training sounders to acquire the remaining coefficient matrix of the column subspace. By dividing the estimation task into two stages, the training sounders for the second stages are only targeted for the column subspace, which will save the channel uses and the computational complexity as well.
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PDF链接：
https://arxiv.org/pdf/1805.11972