Resource Allocation for Semantic Communication in NTN Downlinks

Non-terrestrial networks (NTNs) are vital in providing broad coverage and unimpeded connectivity for the forthcoming sixth-generation (6G) technology. Traditional communication methods centered on the transmission process have approached the Shannon limit; substantial data volumes and large-scale fading will lead to a tangible communication bottleneck, such as increased latency concerns. Semantic communication (SemCom), which focuses on context-related meaning, presents a new paradigm for serving forthcoming NTNs. However, the problem of efficient resource allocation throughout semantic processing and transmission remains unresolved. In this study, a resource allocation and semantic information selection method for low-earth orbit (LEO) satellite-based image transmission is investigated, and a novel semantic-level performance metric termed quality of semantic (QoSem), reflecting the inherent quality of semantic information being transmitted, is defined to measure the performance of SemCom-based NTNs. Owing to NTN’s harsh channels and constrained resources, LEO satellite can only transmit critical semantic information to each high-altitude platform (HAP) so as to meet the QoSem requirement by selecting an appropriate resource to allocate. For the purpose of maximizing the overall QoSem, the selection of semantic information and allocation of computational, transmit power, and bandwidth resources are jointly optimized. To deal with the tricky resource allocation optimization problem with non-linear programming and coupled variables, a novel deep reinforcement learning (DRL)-based intelligent resource allocation algorithm, namely the mixed-action decision intelligent semantic-aware soft-actor critic (MADIS-SAC), is proposed. The evaluation results highlight that, when compared to other baselines, our method extensively enhances QoSem under dynamic situations with severely constrained resources.