A variety of deep learning schemes have endeavoured to integrate deep neural networks (DNNs) into channel coded systems by jointly designing DNN and the channel coding scheme in specific channels. However, this leads to limitations concerning the choice of both the channel coding scheme and the channel paramters. We circumvent these impediments and conceive a turbo-style multi-carrier auto-encoder (MC-AE) for orthogonal frequency-division multiplexing (OFDM) systems, which is the first one that achieves the flexible integration of DNN into any given channel coded systems while achieving an iteration gain. More explicitly, first of all, we design the MC-AE independently of both the channel coding arrangement and of the channel model, where the output layer of the MC-AE decoder is designed for both accepting and producing reliable soft-bit decisions. Owing to the fact that bit-dependency is imposed by the MC-AE mapping, our bespoke MC-AE decoder becomes capable of achieving a beneficial iteration gain, when the extrinsic information is exchanged between the soft-decision MC-AE decoder and the soft-decision channel decoder. Secondly, in order to be able to interpret the performance advantages of our MC-AE over the conventional OFDM, we map the MC-AE’s input-output relationship to an equivalent model-based representation. The associated theoretical analysis verifies the fact that during the process of data-driven signal reconstruction across OFDM’s subcarriers, a beneficial frequency diversity gain is achieved by the proposed MC-AE design. Finally, our simulation results demonstrate that the MC-AE is capable of achieving substantial performance advantages over both conventional OFDM and OFDM based index modulation (OFDM-IM) in channel coded systems.

中文翻译：

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多载波无线系统的 Turbo 检测辅助自动编码器：将深度学习集成到信道编码系统中


各种深度学习方案都致力于通过联合设计 DNN 和特定通道中的通道编码方案，将深度神经网络 (DNN) 集成到通道编码系统中。然而，这导致了关于信道编码方案和信道参数的选择的限制。我们克服了这些障碍，设计了一种用于正交频分复用 (OFDM) 系统的涡轮式多载波自动编码器 (MC-AE)，这是第一个实现 DNN 灵活集成到任何给定信道编码系统中的编码器同时实现迭代增益。更明确地说，首先，我们独立于信道编码安排和信道模型来设计 MC-AE，其中 MC-AE 解码器的输出层被设计用于接受和产生可靠的软比特决策。由于位依赖性是由 MC-AE 映射强加的，当软决策 MC-AE 解码器和软决策 MC-AE 解码器之间交换外部信息时，我们定制的 MC-AE 解码器能够实现有益的迭代增益。 -决策通道解码器。其次，为了能够解释我们的 MC-AE 相对于传统 OFDM 的性能优势，我们将 MC-AE 的输入输出关系映射到等效的基于模型的表示。相关的理论分析验证了这样一个事实：在 OFDM 子载波上数据驱动的信号重建过程中，所提出的 MC-AE 设计实现了有益的频率分集增益。最后，我们的仿真结果表明，在信道编码系统中，MC-AE 能够比传统 OFDM 和基于 OFDM 的索引调制 (OFDM-IM) 实现显着的性能优势。