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Transformer-Based Detection for Highly Mobile Coded OFDM Systems.
Wang, Leijun; Zhou, Wenbo; Tong, Zian; Zeng, Xianxian; Zhan, Jin; Li, Jiawen; Chen, Rongjun.
Afiliação
  • Wang L; School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
  • Zhou W; School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
  • Tong Z; School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
  • Zeng X; School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
  • Zhan J; Guangdong Provincial Key Laboratory of Big Data Computing, The Chinese University of Hong Kong, Shenzhen 518000, China.
  • Li J; School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
  • Chen R; School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Entropy (Basel) ; 25(6)2023 May 26.
Article em En | MEDLINE | ID: mdl-37372196
ABSTRACT
This paper is concerned with mobile coded orthogonal frequency division multiplexing (OFDM) systems. In the high-speed railway wireless communication system, an equalizer or detector should be used to mitigate the intercarrier interference (ICI) and deliver the soft message to the decoder with the soft demapper. In this paper, a Transformer-based detector/demapper is proposed to improve the error performance of the mobile coded OFDM system. The soft modulated symbol probabilities are computed by the Transformer network, and are then used to calculate the mutual information to allocate the code rate. Then, the network computes the codeword soft bit probabilities, which are delivered to the classical belief propagation (BP) decoder. For comparison, a deep neural network (DNN)-based system is also presented. Numerical results show that the Transformer-based coded OFDM system outperforms both the DNN-based and the conventional system.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article