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摘要翻译：
毫米波massive MIMO具有实现移动数据速率数量级增长的潜力，其紧凑的天线阵列提供了前所未有的空间重用水平的窄可控波束。然而，一个基本的技术瓶颈是面对移动性和阻塞时的快速空间信道估计和波束自适应。最近提出的压缩技术利用了毫米波信道的稀疏性，其开销与主要路径数成线性比例，与阵元数成对数比例，是解决这一问题的一种很有前途的方法。此外，它们可以通过具有低精度相位控制的RF波束形成来实现。然而，这些方法对长期相位相干性作出了现有硬件无法满足的隐含假设。本文提出并评价了一种非相干压缩信道估计技术，该技术可以单独基于接收信号强度(RSS)估计稀疏空间信道，并且与现有硬件兼容。该方法基于级联相位恢复（即从RSS测量恢复复值测量，直到标量倍数）与相干压缩估计。传统的级联方案将两个测量矩阵相乘以获得一个整体矩阵，该矩阵的条目位于连续体中，而我们的方案的一个关键新奇之处在于，我们将整个测量矩阵约束为可使用粗量化伪随机相位来实现，使用矩阵的虚拟分解为测量矩阵的乘积来进行相位恢复和压缩估计。理论和仿真结果表明，与相干方法相比，非相干方法的可扩展性和较低的开销继承了相干方法的可扩展性。
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英文标题：
《Noncoherent compressive channel estimation for mm-wave massive MIMO》
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作者：
Maryam Eslami Rasekh, Upamanyu Madhow
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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：
Millimeter (mm) wave massive MIMO has the potential for delivering orders of magnitude increases in mobile data rates, with compact antenna arrays providing narrow steerable beams for unprecedented levels of spatial reuse. A fundamental technical bottleneck, however, is rapid spatial channel estimation and beam adaptation in the face of mobility and blockage. Recently proposed compressive techniques which exploit the sparsity of mm wave channels are a promising approach to this problem, with overhead scaling linearly with the number of dominant paths and logarithmically with the number of array elements. Further, they can be implemented with RF beamforming with low-precision phase control. However, these methods make implicit assumptions on long-term phase coherence that are not satisfied by existing hardware. In this paper, we propose and evaluate a noncoherent compressive channel estimation technique which can estimate a sparse spatial channel based on received signal strength (RSS) alone, and is compatible with off-the-shelf hardware. The approach is based on cascading phase retrieval (i.e., recovery of complex-valued measurements from RSS measurements, up to a scalar multiple) with coherent compressive estimation. While a conventional cascade scheme would multiply two measurement matrices to obtain an overall matrix whose entries are in a continuum, a key novelty in our scheme is that we constrain the overall measurement matrix to be implementable using coarsely quantized pseudorandom phases, employing a virtual decomposition of the matrix into a product of measurement matrices for phase retrieval and compressive estimation. Theoretical and simulation results show that our noncoherent method scales almost as well with array size as its coherent counterpart, thus inheriting the scalability and low overhead of the latter.
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PDF链接：
https://arxiv.org/pdf/1801.06608
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