Envisioned as a promising component of the future wireless Internet-of-Things (IoT) networks, the non-orthogonal multiple access (NOMA) technique can support massive connectivity with a significantly increased spectral efficiency. Cooperative NOMA is able to further improve the communication reliability of users under poor channel conditions. However, the conventional system design suffers from several inherent limitations and is not optimized from the bit error rate (BER) perspective. In this article, we develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL). We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner. On this basis, we construct multiple loss functions to quantify the BER performance and propose a novel multi-task oriented two-stage training method to solve the end-to-end training problem in a self-supervised manner. The learning mechanism of each DNN module is then analyzed based on information theory, offering insights into the explainable DNN architecture and its corresponding training method. We also adapt the proposed scheme to handle the power allocation (PA) mismatch between training and inference and incorporate it with channel coding to combat signal deterioration. Simulation results verify its advantages over orthogonal multiple access (OMA) and the conventional cooperative NOMA scheme in various scenarios.

中文翻译：

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合作 NOMA 的深度多任务学习：系统设计和原理

作为未来无线物联网 (IoT) 网络的一个有前途的组件，非正交多址 (NOMA) 技术可以支持大规模连接，同时显着提高频谱效率。合作NOMA能够进一步提高用户在较差信道条件下的通信可靠性。然而，传统的系统设计受到一些固有的限制，并且没有从误码率 (BER) 的角度进行优化。在本文中，我们利用深度学习 (DL) 的最新进展开发了一种新颖的深度合作 NOMA 方案。我们开发了一种新颖的混合级联深度神经网络 (DNN) 架构，以便可以整体优化整个系统。以这个为基础，我们构建了多个损失函数来量化 BER 性能，并提出了一种新的面向多任务的两阶段训练方法，以自监督的方式解决端到端的训练问题。然后基于信息论分析每个 DNN 模块的学习机制，深入了解可解释的 DNN 架构及其相应的训练方法。我们还调整了所提出的方案来处理训练和推理之间的功率分配 (PA) 不匹配，并将其与信道编码相结合以对抗信号恶化。仿真结果验证了其在各种场景下优于正交多址 (OMA) 和传统协作 NOMA 方案的优势。然后基于信息论分析每个 DNN 模块的学习机制，深入了解可解释的 DNN 架构及其相应的训练方法。我们还调整了所提出的方案来处理训练和推理之间的功率分配 (PA) 不匹配，并将其与信道编码相结合以对抗信号恶化。仿真结果验证了其在各种场景下优于正交多址 (OMA) 和传统协作 NOMA 方案的优势。然后基于信息论分析每个 DNN 模块的学习机制，深入了解可解释的 DNN 架构及其相应的训练方法。我们还调整了所提出的方案来处理训练和推理之间的功率分配 (PA) 不匹配，并将其与信道编码相结合以对抗信号恶化。仿真结果验证了其在各种场景下优于正交多址 (OMA) 和传统协作 NOMA 方案的优势。