用于干扰对准的学习序列信道选择 可重构天线

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摘要翻译：
近年来，机器学习技术被用来支持、增强或增强无线系统，特别是在协议栈的物理层。由于算法的复杂性、对现有训练数据的依赖和/或分布式设置，传统的基于ML的方法或优化往往不适合。本文将多用户无线网络中基于可重构天线的干扰对准信道选择问题描述为一个学习问题。更具体地说，我们提出通过使用序列学习，可以选择一个有效的信道或信道组合，以便使用可重构天线来增强干扰对准。我们首先将信道选择问题描述为一个以优化网络和速率为目标的多臂问题。我们证明，通过使用自适应顺序学习策略，网络中的每个节点都可以学习选择最优信道，而不需要对所有可用的天线状态进行完全和瞬时的CSI。在MIMO干扰信道下，我们使用传统的IA方案对我们的技术进行了性能分析，并量化了模式分集和学习信道选择的好处。
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英文标题：
《Learning Sequential Channel Selection for Interference Alignment using
  Reconfigurable Antennas》
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作者：
Nikhil Gulati, Rohit Bahl, Kapil R. Dandekar
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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：
  In recent years, machine learning techniques have been explored to support, enhance or augment wireless systems especially at the physical layer of the protocol stack. Traditional ML based approach or optimization is often not suitable due to algorithmic complexity, reliance on existing training data and/or due to distributed setting. In this paper, we formulate a reconfigurable antenna based channel selection problem for interference alignment in a multi-user wireless network as a learning problem. More specifically, we propose that by using sequential learning, an effective channel or combination of channels can be selected in order to enhance interference alignment using reconfigurable antennas. We first formulate the channel selection as a multi-armed problem that aims to optimize the sum rate of the network. We show that by using an adaptive sequential learning policy, each node in the network can learn to select optimal channels without requiring full and instantaneous CSI for all the available antenna states. We conduct performance analysis of our technique for a MIMO interference channel using a conventional IA scheme and quantify the benefits of pattern diversity and learning channel selection.
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PDF链接：
https://arxiv.org/pdf/1712.06181

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关键词：Optimization Applications Conventional Application performance 选择 对准 优化 序列