URLLC Service in UAV Rate-Splitting Multiple Access: Adapting Deep Learning Techniques for Wireless Network

The 3GPP standard defines the requirements for next-generation wireless networks, with particular attention to Ultra-Reliable Low-Latency Communications (URLLC), critical for applications such as Unmanned Aerial Vehicles (UAVs). In this context, Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technique to improve spectrum efficiency and user fairness by allowing multiple users to share the same frequency resources. However, optimizing key parameters–such as beamforming, rate allocation, and UAV trajectory–presents significant challenges due to the nonconvex nature of the problem, especially under stringent URLLC constraints. This paper proposes an advanced deep learning-driven approach to address the resulting complex optimization challenges. We formulate a downlink multiuser UAV, Rate-Splitting Multiple Access (RSMA), and Multiple Input Multiple Output (MIMO) system aimed at maximizing the achievable rate under stringent constraints, including URLLC quality-of-service (QoS), power budgets, rate allocations, and UAV trajectory limitations. Due to the highly nonconvex nature of the optimization problem, we introduce a novel distributed deep reinforcement learning (DRL) framework based on dual-agent deep deterministic policy gradient (DA-DDPG). The proposed framework leverages inception-inspired and deep unfolding architectures to improve feature extraction and convergence in beamforming and rate allocation. For UAV trajectory optimization, we design a dedicated actor-critic agent using a fully connected deep neural network (DNN), further enhanced through incremental learning. Simulation results validate the effectiveness of our approach, demonstrating significant performance gains over existing methods and confirming its potential for real-time URLLC in next-generation UAV communication networks.