This paper proposes a deep learning (DL)-aided multicarrier (MC) system operating on fading channels, where both modulation and demodulation blocks are modeled by deep neural networks (DNNs), regarded as the encoder and decoder of an autoencoder (AE) architecture, respectively. Unlike existing AE-based systems, which incorporate domain knowledge of a channel equalizer to suppress the effects of wireless channels, the proposed scheme, termed as MC-AE, directly feeds the decoder with the channel state information and received signal, which are then processed in a fully data-driven manner. This new approach enables MC-AE to jointly learn the encoder and decoder to optimize the diversity and coding gains over fading channels. In particular, the block error rate of MC-AE is analyzed to show its higher performance gains than existing hand-crafted baselines, such as various recent index modulation-based MC schemes. We then extend MC-AE to multiuser scenarios, wherein the resultant system is termed as MU-MC-AE. Accordingly, two novel DNN structures for uplink and downlink MU-MC-AE transmissions are proposed, along with a novel cost function that ensures a fast training convergence and fairness among users. Finally, simulation results are provided to show the superiority of the proposed DL-based schemes over current baselines, in terms of both the error performance and receiver complexity.

中文翻译：

---

深度学习辅助多载波系统

本文提出了一种在衰落信道上运行的深度学习 (DL) 辅助多载波 (MC) 系统，其中调制和解调块都由深度神经网络 (DNN) 建模，被视为自动编码器 (AE) 架构的编码器和解码器， 分别。与现有的基于 AE 的系统不同，这些系统结合了信道均衡器的领域知识来抑制无线信道的影响，所提出的方案，称为 MC-AE，直接将信道状态信息和接收信号馈送到解码器，然后对其进行处理以完全数据驱动的方式。这种新方法使 MC-AE 能够联合学习编码器和解码器，以优化衰落信道上的分集和编码增益。特别是，分析了 MC-AE 的块错误率，以显示其比现有手工制作的基线更高的性能增益，例如各种最近的基于索引调制的 MC 方案。然后我们将 MC-AE 扩展到多用户场景，其中产生的系统称为 MU-MC-AE。因此，提出了用于上行链路和下行链路 MU-MC-AE 传输的两种新颖 DNN 结构，以及一种新颖的成本函数，可确保用户之间的快速训练收敛和公平性。最后，提供了仿真结果以显示所提出的基于 DL 的方案在错误性能和接收器复杂性方面优于当前基线的优越性。提出了用于上行链路和下行链路 MU-MC-AE 传输的两种新颖的 DNN 结构，以及一种新颖的成本函数，可确保用户之间的快速训练收敛和公平性。最后，提供了仿真结果以显示所提出的基于 DL 的方案在错误性能和接收器复杂性方面优于当前基线的优越性。提出了用于上行链路和下行链路 MU-MC-AE 传输的两种新颖的 DNN 结构，以及一种新颖的成本函数，可确保用户之间的快速训练收敛和公平性。最后，提供了仿真结果以显示所提出的基于 DL 的方案在错误性能和接收器复杂性方面优于当前基线的优越性。