信息通信技术与政策

信息通信技术与政策

信息通信技术与政策 ›› 2019, Vol. 45 ›› Issue (7): 57-61.

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深度学习在物理层信号处理中的应用研究

Deep learning in physical layer signal detection

  

  • 出版日期:2019-07-15 发布日期:2020-11-26
  • 作者简介:
    刘姿杉:中国信息通信研究院技术与标准研究所工程师

  • Online:2019-07-15 Published:2020-11-26

摘要: 目前针对5G通信高可靠和超高容量无线通信的需求已经引起了广泛关注与研究。然而,当前的通信系统依然受制于传统的通信理论而限制了网络性能的突破性提升。作为一项广受关注和应用的新兴技术,深度学习已经被证实有潜力处理复杂通信系统中的通信问题并提高通信性能。本文主要介绍基于深度学习的物理层应用,并提出一种基于深度Q网络(DQN)的MIMO系统位置信息验证方案,接收者在多变未知的信道环境下利用深度Q网络不断更新信号检测阈值,提高信号检测的准确度,实现对发送者位置信息的确认。

关键词: 深度学习, 信号检测, MIMO

Abstract: Rcently, the demand for ultra- reliable low latency communication for the 5G communication system has attracted extensive attention and research effort. However, the conventional communication systems are still limited to the tradtional communication theories, and lack hugh breakthrough in the network performance. As a promising machine learning tool to handle the accurate pattern recognition from complex raw data, deep learning (DL) is becoming a powerful method to handle communication problems, add intelligence to wireless networks with large-scale topology and complex
radio conditions. This paper mainly introduces the physical layer application based on deep learning, and proposes a deep Q-network (DQN) based MIMO system location information verification scheme to improve the accuracy of signal detection in a varing and unknown channel environment.

Key words: deep learning, signal detection, MIMO