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

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

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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
Pages (from-to)672-682
Number of pages11
JournalIEEE Transactions on Cognitive Communications and Networking
Issue number2
Publication statusPublished - 2022 Jun 1


  • Deep learning
  • Fully convolutional neural network
  • Limited feedback
  • Massive MIMO
  • Quantization

ASJC Scopus subject areas

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


Dive into the research topics of 'Fully Convolutional Neural Network-Based CSI Limited Feedback for FDD Massive MIMO Systems'. Together they form a unique fingerprint.

Cite this