Next generation communications demand for better spectrum management, lower latency, and guaranteed quality-of-service (QoS). Recently, Artificial intelligence (AI) has been widely introduced to advance these aspects in next generation wireless systems. However, such AI applications suffer from limited training data, low robustness, and poor generalization capabilities. To address these issues, a model-driven deep unfolding (DU) algorithm is introduced in this paper to bridge the gap between traditional model-driven communication algorithms and data-driven deep learning. Focusing on the QoS-aware rate-splitting multiple access (RSMA) resource allocation problem in multi-user communications, a conventional fractional programming (FP) algorithm is first applied as a benchmark. The solution is then refined by the application of projection gradient descent (PGD). DU is employed to further speed up convergence procedure, hence improving the efficiency of PGD. Moreover, the feasibility of results is guaranteed by designing a low-complexity projection based on scale factors, plus adding violation control mechanisms into the loss function that minimizes error rates. Finally, we provide a detailed analysis of the computational complexity and analysis design of the proposed DU algorithm. Extensive simulations are conducted and the results demonstrate that the proposed DU algorithm can reach the optimal communication efficiency with a mere $0.024\%$ violation rate for 4 layers DU. The DU algorithm also exhibits robustness in out-of-distribution tests and can be effectively trained with as few as 50 samples.

翻译：下一代通信系统对频谱管理、时延保障和服务质量(QoS)提出了更高要求。近年来，人工智能技术被广泛引入以提升新一代无线系统的这些性能维度。然而，此类人工智能应用存在训练数据有限、鲁棒性不足和泛化能力薄弱等问题。为应对这些挑战，本文引入了一种模型驱动的深度展开算法，以弥合传统模型驱动通信算法与数据驱动深度学习之间的鸿沟。聚焦于多用户通信中服务质量感知的速率分割多址资源分配问题，首先采用传统分数规划算法作为基准方案。继而通过投影梯度下降法对解进行优化。深度展开算法的引入进一步加速了收敛过程，从而提升了投影梯度下降法的计算效率。此外，通过设计基于缩放因子的低复杂度投影算子，并在损失函数中融入约束违反控制机制以最小化误差率，确保了结果的可行性。最后，我们对所提深度展开算法的计算复杂度与架构设计进行了详细分析。大量仿真实验表明，所提出的深度展开算法在4层架构下仅产生0.024%的约束违反率即可达到最优通信效率。该算法在分布外测试中展现出良好的鲁棒性，且仅需50个样本即可完成有效训练。