Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 230200219-5.doi: 10.11896/jsjkx.230200219

• Network & Communication • Previous Articles     Next Articles

Design of QPSK Intelligent Receiver Based on LSTM Neural Network

ZHU Li1, HAN Huimei1, ZHAI Wenchao2   

  1. 1 College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China
    2 College of Information Engineering,China Jiliang University,Hangzhou 310018,China
  • Published:2023-11-09
  • About author:ZHU Li,born in 2002.His main research interests include communication receiver and deep learning.
    HAN Huimen,born in 1990,Ph.D,gra-duate supervisor.Her main research interests include random access schemes for massive MIMO systems,machine-to-machine communications,machine learning,intelligent reflecting surface,and federated learning.
  • Supported by:
    National Natural Science Foundation of China(62001419).

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Abstract: To solve the problem of low detection accuracy and high complexity of quadrature phase shift keying(QPSK) receiver,this paper proposes a QPSK intelligent receiver based on long short-term memory(LSTM) neural network.The proposed intelligent receiver consists of LSTM and fully connected layers,which employs the LSTM to capture the temporal correlation of the received signal.Furthermore,the proposed intelligent receiver has low complexity.Simulation results show that,compared with the existing QPSK receivers,the proposed QPSK intelligent receiver significantly improves detection performance in the scenarios of additive Gaussian white noise,inphase quadrature imbalance and frequency deviation channel.

Key words: Deep learning, Long short-term memory neural network, Intelligent receiver, QPSK modulation

CLC Number: 

  • TN919.3
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