TC-MIMONet: A Learning-based Transceiver for MIMO Systems with Temporal Correlations

Chunhui Chen, Zihao Wang, Yuyi Mao, Hao Wu, Bo Bai, Gong Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Data-driven approaches have recently emerged as promising remedies for communication system designs, which leverage deep learning techniques for automated development and optimization. In this paper, we revisit the designs of multi-input multi-output (MIMO) wireless systems and investigate the end-to-end learning for MIMO systems with temporal correlations. Our objective is to develop a MIMO transceiver to improve the communication performance by making fully use of the available temporal information. Although the end-to-end learning framework has been applied to various communication systems, existing designs largely rely on memoryless autoencoders (AEs) and overlook the time dependency. To overcome this issue, we propose a novel learning-based MIMO transceiver, namely, the TC-MIMONet, which extends the conventional memoryless AE-based transceivers by customizing two neural network components with memory. In particular, a long short-term memory (LSTM)-based CSI predictor is adopted at the transmitter, while a two-timescale LSTM-based decoder is developed for the receiver. Simulation results show that TC-MIMONet achieves significant block error rate reduction compared to two baseline schemes without utilizing the available temporal information.
Original languageEnglish
Title of host publication2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings
Place of PublicationHelsinki, Finland
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728189642
DOIs
Publication statusPublished - Apr 2021

Publication series

NameIEEE Vehicular Technology Conference
Volume2021-April
ISSN (Print)1550-2252

Keywords

  • Deep learning
  • autoencoder (AE)
  • long short-term memory (LSTM)
  • multi-input multi-output (MIMO)
  • temporal correlations

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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