Signal Detection in Uplink Time-Varying OFDM Systems Using RNN with Bidirectional LSTM

Shengyao Wang, Rugui Yao, Theodoros A. Tsiftsis, Nikolaos I. Miridakis, Nan Qi

Research output: Contribution to journalArticlepeer-review


In this letter, we propose a deep learning-assisted approach for signal detection in uplink orthogonal frequency-division multiplexing (OFDM) systems over time-varying channels. In particular, we utilize a recurrent neural network (RNN) with bidirectional long short-term memory (LSTM) architecture to achieve signal detection. In addition, with the help of convolutional neural network (CNN) and batch normalization (BN), a new network structure CNN-BN-RNN Network (CBR-Net) is proposed to obtain better performance. The sequence feature information of the OFDM received signal is extracted from big data to successfully train a RNN-based signal detection model, which simplifies the architecture of OFDM systems and can adapt to the change of channel paths. Simulation results also demonstrate that the trained RNN model has the ability to recall the characteristics of wireless time-varying channels and provide accurate and robust signal recovery performance.

Original languageEnglish
Article number9140031
Pages (from-to)1947-1951
Number of pages5
JournalIEEE Wireless Communications Letters
Issue number11
Publication statusPublished - Nov 2020


  • Deep learning (DL)
  • OFDM
  • RNN
  • signal detection
  • time-varying channels

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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