Towards Transparent Deep Learning: Neural Precoder Design for Downlink RSMA - Wang, Chao; Li, Guo; Li, Zan; Zhang, Haibin; Jin, Liang (IEEE transactions on communications, 2025)
The non-convexity of rate-splitting precoder design precludes the direct use of efficient convex optimization algorithms. Instead, successive convex approximation (SCA)-based methods have emerged as a promising approach for precoder design in rate-splitting multiple access (RSMA) systems. Although SCA-based algorithms deliver satisfactory performance, their lengthy optimization process and high computational complexity-due to repeatedly solving approximate problems-hinder real-time implementation. To address this challenge, we propose an alternating direction method of multipliers (ADMM)-induced deep learning network (AIDLN)-based precoder design for multiuser downlink communications, leveraging the strengths of both model-based optimization and data-driven deep learning. Specifically, we first develop an iterative precoding algorithm based on ADMM, where each subproblem admits a closed-form solution. Next, we unfold the iterations of this ADMM-based algorithm into the layers of a neural network, constructing a deep-unfolding architecture. To accelerate convergence, we introduce trainable parameters alongside the ADMM hyperparameters. Numerical results demonstrate that our AIDLN-based precoder achieves superior performance compared to traditional model-based optimization while maintaining low computational complexity, making it suitable for real-time applications.