Deep Learning-Based Space Shift Keying Systems

  • Yue Zhang
  • Xuesi Wang
  • Jintao WangEmail author
  • Yonglin Xue
  • Jian Song
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 286)


To handle the performance degradation of space shift keying (SSK) systems under practical non-Gaussian channels, we propose a deep neural network model in which an auto-encoder (AE) is developed to design proper constellations and corresponding demodulation. With full knowledge of channel statistics, the transmitter and receiver are jointly optimized in our scheme. By representing the SSK system as an AE, we consider the cross-entropy loss function for antenna index and formulate the overall pipeline using deep learning techniques. Moreover, our implementation can be adopted in several noise conditions successfully. Results confirm that our model outperforms the maximum likelihood (ML) detection scheme in terms of block error rates (BLER).


Space shift keying (SSK) Deep learning Neural network 


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Yue Zhang
    • 1
  • Xuesi Wang
    • 1
  • Jintao Wang
    • 1
    Email author
  • Yonglin Xue
    • 1
  • Jian Song
    • 1
  1. 1.Tsinghua UniversityBeijingPeople’s Republic of China

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