Deep Learning-Based Codebook Designs for Generalized Space Shift Keying Systems

Di Huang, Xue Qin Jiang, Inkyu Lee, Han Hai

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this paper, we propose a novel deep learning (DL)-based codebook design method for generalized space shift keying (GSSK) systems. In this DL-based method, the transmitter and receiver of GSSK systems are designed based on deep neural network (DNN). By training the DNN in an end-to-end manner, the DL-based method can adaptively generate suitable binary codewords and combine them into a codebook for GSSK systems. Simulation results show that the proposed DL-based method obtains better performance compared to conventional approaches.

Original languageEnglish
Pages (from-to)1038-1042
Number of pages5
JournalIEEE Transactions on Vehicular Technology
Volume71
Issue number1
DOIs
Publication statusPublished - 2022 Jan 1

Bibliographical note

Publisher Copyright:
© 1967-2012 IEEE.

Keywords

  • Generalized space shift keying
  • codebook
  • deep learning
  • multiple-input multiple-output

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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