Compressive Sampled CSI Feedback Method Based on Deep Learning for FDD Massive MIMO Systems

Jie Wang, Guan Gui, Tomoaki Ohtsuki, Bamidele Adebisi, Haris Gacanin, Hikmet Sari

研究成果: Article査読

22 被引用数 (Scopus)


Accurate downlink channel state information (CSI) is required to be fed back to the base station (BS) in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems in order to achieve maximum antenna diversity and multiplexing. However, downlink CSI feedback overhead scales with the number of transceiver antennas, a major hurdle for practical deployment of FDD massive MIMO systems. To solve this problem, we propose a compressive sampled CSI feedback method based on deep learning (SampleDL). In SampleDL, the massive MIMO channel matrix is sampled uniformly in time/frequency dimension before being fed into neural networks (NNs), which will reduce the computational resource/time at user equipment (UE) as well as enhance the CSI recovery accuracy at the BS. Both theoretical analysis and normalized mean square errors (NMSE) results confirm the advantages of the proposed method in terms of time complexity and recovery accuracy. Besides, a suitable CSI feedback period is explored by link level simulations, which aims to further reduce the overhead of CSI feedback without degrading the communication quality.

ジャーナルIEEE Transactions on Communications
出版ステータスPublished - 2021 9月

ASJC Scopus subject areas

  • 電子工学および電気工学


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