Rate-splitting multiple access (RSMA) has been proven as an effective communication scheme for 5G and beyond. However, current approaches to RSMA resource management require complicated iterative algorithms, which cannot meet the stringent latency requirement by users with limited resources. Recently, data-driven methods are explored to alleviate this issue. However, they suffer from poor generalizability and scarce training data to achieve satisfactory performance. In this paper, we propose a fractional programming (FP) based deep unfolding (DU) approach to address resource allocation problem for a weighted sum rate optimization in RSMA. By carefully designing the penalty function, we couple the variable update with projected gradient descent algorithm (PGD). Following the structure of PGD, we embed a few learnable parameters in each layer of the DU network. Through extensive simulation, we have shown that the proposed model-based neural networks can yield similar results compared to the traditional optimization algorithm for RSMA resource management but with much lower computational complexity, less training data, and higher resilience to out-of-distribution (OOD) data.

翻译：速率分割多址接入（RSMA）已被证明是5G及未来通信系统的有效方案。然而，当前RSMA资源管理方法需要复杂的迭代算法，难以满足资源有限用户对时延的严苛要求。近期，数据驱动方法被探索用于缓解此问题，但其存在泛化能力差、训练数据稀缺导致性能不足的缺陷。本文提出一种基于分式规划（FP）的深度展开（DU）方法，以解决RSMA中加权和速率优化的资源分配问题。通过精心设计惩罚函数，我们将变量更新与投影梯度下降算法（PGD）相结合。依据PGD的结构，我们在DU网络的每一层中嵌入少量可学习参数。大量仿真表明，所提出的基于模型的神经网络能够在RSMA资源管理中取得与传统优化算法相近的结果，同时具有更低的计算复杂度、更少的训练数据需求，以及对分布外（OOD）数据更强的鲁棒性。