基于深度学习的频分复用大规模多输入多输出下行信道状态信息获取技术

Frequency Division Duplex Massive Multiple-input Multiple-output Downlink Channel State Information Acquisition Techniques Based on Deep Learning

大规模多输入多输出(Multiple input multiple output,MIMO)技术的演进是第6代(The sixth generation,6G)无线通信系统性能进一步提升的重要支撑。随着天线阵列规模的持续扩大,频分复用(Fvequency division duplexing,FDD)大规模MIMO系统获取下行信道状态信息(Channel state information,CSI)面临着严峻挑战。深度学习具有强大的学习及处理高维数据的能力,能够为解决这一挑战提供新的方案。本文综述了基于深度学习的FDD大规模MIMO下行CSI获取技术,包括CSI反馈和预测技术。首先给出了基于深度学习的CSI反馈和预测的原理框架,其次分析比较了国内外相关研究成果的优越性能,为解决面向6G的FDD大规模MIMO系统获取下行CSI问题提供了可行的参考方案。最后讨论了FDD大规模MIMO下行CSI获取的有待进一步解决的开放性问题以及所对应的潜在研究方案。

The evolution of massive multiple-input multiple-output(MIMO)techniques is an important support for further improving the performance of six-generation(6G)wireless communication systems.However,with the continuous expansion of large-scale antenna arrays,frequency division duplex(FDD)massive MIMO systems are facing severe challenges in acquiring downlink channel state information(CSI).Deep learning has a powerful ability to learn and process high-dimensional data,which provides a potential solution to this challenge.In this paper,we survey FDD massive MIMO downlink CSI acquisition techniques based on deep learning,including CSI feedback and prediction techniques.Firstly,the theoretical frameworks of CSI feedback and prediction based on deep learning are presented.Then,the superior performance of relevant research results at home and abroad is analyzed,providing a reference scheme for solving the problem of acquiring downlink CSI in FDD massive MIMO systems towards 6G.Finally,unsolved open problems of FDD massive MIMO downlink CSI acquisition are discussed,followed by potential solutions correspondingly.