Fully Convolutional Neural Network Based CSI Limited Feedback for FDD Massive MIMO Systems

Guanghui Fan, Jinlong Sun, Bamidele Adebisi, Tomoaki Ohtsuki, Guan Gui, Haris Gacanin, Hikmet Sari

Research output: Contribution to journalArticlepeer-review

Abstract

Due to the lack of channel reciprocity in frequency division duplexity (FDD) massive multiple-input multiple-output (MIMO) systems, it is impossible to infer the downlink channel state information (CSI) directly from its reciprocal uplink CSI. Hence, the estimated downlink CSI needs to be continuously fed back to the base station (BS) from the user equipment (UE), consuming valuable bandwidth resources. This is exacerbated, in massive MIMO, with the increase of the antennas at the BS. This paper propose a fully convolutional neural network (FullyConv) to compress and decompress the downlink CSI. FullyConv will improve the reconstruction accuracy of downlink CSI and reduce the training parameters and computational resources. Besides, we add a quantization module in the encoder and a dequantization module in the decoder of the FullyConv to simulate a real feedback scenario. Experimental results demonstrate that the proposed FullyConv is better than the baseline on reconstruction performance and reduction of the storage and computational overhead. Furthermore, the FullyConv added quantization and dequantization modules is robust to quantization error in real feedback scenarios.

Original languageEnglish
JournalIEEE Transactions on Cognitive Communications and Networking
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Antenna feeds
  • Channel models
  • Computer architecture
  • deep learning
  • Downlink
  • Fully convolutional neural network
  • limited feedback
  • Massive MIMO
  • massive MIMO
  • Quantization (signal)
  • quantization.
  • Uplink

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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