State-Optimization-Based Semantic SCMA Broadcast With Deep Reinforcement Learning in UAV-Assisted IoV Networks

This paper proposes a semantic Sparse Code Multiple Access (SCMA) broadcast framework for Unmanned Aerial Vehicle (UAV)-assisted Internet of Vehicles (IoV) networks, integrating structural state optimization with Lyapunov-guided Deep Reinforcement Learning (DRL). The UAV acts as a semantic-aware aerial Base Station (aBS) that extracts and encodes high-level semantic features before mapping them onto the SCMA factor graph. A state-optimization module designs the semantic projection and energy-mapping matrices to preserve semantic fidelity under power and sparsity constraints. On top of this structure, a Lyapunov-constrained DRL controller adapts the UAV’s transmit-power policy based on instantaneous semantic reliability, channel dynamics, and network conditions. Simulation results demonstrate that the proposed Lyapunov-guided learning approach significantly improves energy efficiency and long-term stability while maintaining semantic reliability above the required threshold, with negligible throughput degradation compared to unconstrained SCMA-based learning schemes.