Wang Leijun, Zhou Wenbo, Tong Zian, Zeng Xianxian, Zhan Jin, Li Jiawen, Chen Rongjun
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Guangdong Provincial Key Laboratory of Big Data Computing, The Chinese University of Hong Kong, Shenzhen 518000, China.
Entropy (Basel). 2023 May 26;25(6):852. doi: 10.3390/e25060852.
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.
本文关注的是移动编码正交频分复用（OFDM）系统。在高速铁路无线通信系统中，应使用均衡器或检测器来减轻载波间干扰（ICI），并通过软解映射器将软消息传递给解码器。本文提出了一种基于Transformer的检测器/解映射器，以提高移动编码OFDM系统的差错性能。由Transformer网络计算软调制符号概率，然后用于计算互信息以分配码率。接着，网络计算码字软比特概率，并将其传递给经典的置信传播（BP）解码器。为作比较，还给出了一种基于深度神经网络（DNN）的系统。数值结果表明，基于Transformer的编码OFDM系统性能优于基于DNN的系统和传统系统。
