AI Article Synopsis

  • * It introduces a Transformer-based detector/demapper that reduces intercarrier interference (ICI) and enhances error performance by computing soft modulated symbol probabilities and mutual information for code rate allocation.
  • * The results indicate that the Transformer-based approach significantly outperforms both a deep neural network (DNN)-based system and traditional methods in terms of effectiveness.

Article 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297721PMC
http://dx.doi.org/10.3390/e25060852DOI Listing

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