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arXiv 1812.00541cs.ITcs.LGmath.IT
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利用无线信道状态信息结构超越线性相关性:一种深度学习方法

Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach

Shanghai Institute for Advanced Communication and Data Science(上海先进通信与数据科学研究院);Shanghai University(上海大学);Beijing National Research Center for Information Science and Technology(北京信息科学与技术国家研究中心);Tsinghua University(清华大学);University of Southern California(南加州大学);Intel Corporation(英特尔公司)

Zhiyuan Jiang, Sheng Chen, Andreas F. Molisch, Rath Vannithamby, Sheng Zhou, Zhisheng Niu

摘要:

关于无线系统在其中操作的传播信道的信息的知识使得能够实现用于信号传输的更好、更有效的方法。因此,信道状态信息(CSI)对系统性能起着至关重要的作用。CSI的重要性实际上在即将到来的5G及以后的系统中正在增长,例如,用于实现大规模多输入多输出(MIMO)。然而,及时和准确的CSI的获取长期以来一直被认为是一个主要问题,并且由于需要在大规模MIMO系统中获得许多天线元件的CSI而变得越来越具有挑战性。为了应对这一挑战,现有的工作主要集中在利用线性结构的CSI,如CSI在空间域中的相关性,以实现降维。在这篇文章中,我们首先系统地回顾了CSI结构开发的最新技术,然后扩展到寻求更深的结构,使远程CSI推理,其中数据驱动的深度神经网络(DNN)的方法是必要的,由于模型不足。我们开发适合CSI数据的特定DNN设计。提供的案例研究,以证明在这个方向的未来性能增强的巨大潜力。

英文摘要:

Knowledge of information about the propagation channel in which a wireless system operates enables better, more efficient approaches for signal transmissions. Therefore, channel state information (CSI) plays a pivotal role in the system performance. The importance of CSI is in fact growing in the upcoming 5G and beyond systems, e.g., for the implementation of massive multiple-input multiple-output (MIMO). However, the acquisition of timely and accurate CSI has long been considered as a major issue, and becomes increasingly challenging due to the need for obtaining CSI of many antenna elements in massive MIMO systems. To cope with this challenge, existing works mainly focus on exploiting linear structures of CSI, such as CSI correlations in the spatial domain, to achieve dimensionality reduction. In this article, we first systematically review the state-of-the-art on CSI structure exploitation; then extend to seek for deeper structures that enable remote CSI inference wherein a data-driven deep neural network (DNN) approach is necessary due to model inadequacy. We develop specific DNN designs suitable for CSI data. Case studies are provided to demonstrate great potential in this direction for future performance enhancement.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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