QoSem-based Resource Allocation for Semantic Communication in NTN Downlinks

Non-terrestrial networks (NTNs) are crucial for 6G communications but face challenges like classical communication bottlenecks. Semantic communication (SemCom) offers a solution, but efficient resource allocation remains unresolved. This paper proposes a joint approach for resource allocation and semantic information selection in low-earth orbit (LEO) satellite-based image transmission. A novel metric, Quality of Semantic (QoSem), is introduced to measure the SemCom performance. An optimization problem is formulated to maximize QoSem by allocating computing, power, and bandwidth resources and selecting optimal semantic information. Furthermore, we propose a novel deep reinforcement learning (DRL) algorithm, namely Mixed-Action Decision Intelligent Semantic-aware Soft-Actor Critic (MADIS-SAC), to handle the problem’s nonconvexity and NP-hard complexity. Results show 82.5% of QoSem improvement compared to baselines under resource constraints.