CV-3DCNN: Complex-Valued Deep Learning for CSI Prediction in FDD Massive MIMO Systems

Yibin Zhang, Jie Wang, Jinlong Sun, Bamidele Adebisi, Haris Gacanin, Guan Gui, Fumiyuki Adachi

Research output: Contribution to journalArticlepeer-review

Abstract

In beyond fifth-generation (B5G) era, massive multiple-input multiple-output (M-MIMO) will be a key technology to offer higher network capacities. Due to the different frequency of uplink and downlink channels in FDD systems, the channel state information (CSI) feedback from user terminal to the base station is necessary, but this reduces the spectrum efficiency. This letter proposes a deep learning based solution to predict the downlink CSI in frequency division duplex (FDD) systems, which is termed as complex-valued three dimensional convolutional neural network (CV-3DCNN). The proposed network uses a complex-valued neural network in complex domain to deal with the complex CSI matrices, and adopts three-dimensional convolution operations for feature extraction. The proposed scheme aims to make full use of the hidden information of the complex matrices of the CSI data, and to minimize information loss caused by data processing. The experimental results demonstrate that the proposed architecture can improve accuracy of the downlink CSI prediction by approximately 6 dB.

Original languageEnglish
Article number9209095
Pages (from-to)266-270
Number of pages5
JournalIEEE Wireless Communications Letters
Volume10
Issue number2
DOIs
Publication statusPublished - 2021 Feb

Keywords

  • FDD massive MIMO
  • channel state information
  • complex-valued neural network
  • partial channel reciprocity
  • three-dimensional convolutional layer

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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