In this paper, we propose a novel Average autoencoder (AE)-based amplify-and-forward (AF) relay networks impacted by the I/Q imbalance (IQI) and additional hardware impairments (AHI), where the source and destination nodes are equipped with neural network (NN)-based encoder and decoder, while a conventional AF relay node assists the transmission. The average AE employs multiple small NN-based decoders at the destination node, each decoding a soft probabilistic output that is averaged to obtain the final soft probabilistic output at the destination node. By considering multiple small NN decoders, we reduce the implementation complexity significantly while improving the performance compared to the AE with a single large but NN-based decoder. Within this Average AE framework, we propose a coded modulation design (CMD) with zero-forcing-based IQI compensation that considers the availability of the channel state information (CSI) and IQI knowledge. However, the IQI and CSI need to be estimated separately. Thus, we also propose a CMD with no IQI compensation that requires only the CSI knowledge. Finally, we propose a differential CMD that removes the necessity of both the CSI and IQI knowledge. Under low signal-to-interference-and-noise-ratio regimes, we show that the proposed Average AE outperforms the optimal maximum likelihood detector by considerable margin.

中文翻译：

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一种新型的基于平均自动编码器的硬件损伤放大转发中继网络


在本文中，我们提出了一种受 I/Q 不平衡 (IQI) 和附加硬件损伤 (AHI) 影响的新型基于平均自动编码器 (AE) 的放大转发 (AF) 中继网络，其中源节点和目标节点分别是配备基于神经网络（NN）的编码器和解码器，而传统的AF中继节点辅助传输。平均AE在目标节点采用多个基于NN的小型解码器，每个解码器对软概率输出进行平均，以获得目标节点的最终软概率输出。通过考虑多个小型 NN 解码器，与使用单个大型但基于 NN 的解码器的 AE 相比，我们显着降低了实现复杂性，同时提高了性能。在此平均 AE 框架内，我们提出了一种具有基于迫零的 IQI 补偿的编码调制设计 (CMD)，该设计考虑了信道状态信息 (CSI) 和 IQI 知识的可用性。然而，IQI和CSI需要单独估计。因此，我们还提出了一种无需 IQI 补偿、仅需要 CSI 知识的 CMD。最后，我们提出了一种差分 CMD，消除了 CSI 和 IQI 知识的必要性。在低信号干扰比和噪声比的情况下，我们表明所提出的平均 AE 在相当大的程度上优于最佳最大似然检测器。