Sleeping Multi-Armed Bandit-Based Path Selection in Space-Ground Semantic Communication Networks
Hanlu Wu, Xu Yang, Shouxin Cao, Jia Liu, Hiroki Takakura, Norio Shiratori · 2025
Semantic communication, an emerging AI-driven communication paradigm, offers great potential for multimodal data delivery in space-ground integrated networks (SGINs). However, the dynamic nature of SGINs presents severe challenges for path selection, making it difficult to ensure the quality of service (QoS) at the semantic level. To this end, we propose in this paper a novel path selection scheme in space-ground multimodal semantic communication networks based on the sleeping multi-armed bandit (MAB) approach. Specifically, we first model the approximate semantic entropy and semantic rate, formulating an optimal path selection problem that integrates link state information and semantic data transmission volume. Then, we convert the path selection problem into a sleeping MAB problem and meticulously design an upper confidence bound (UCB)-based algorithm to solve it, called Periodic Probability Sleeping Path Selection (PPSPS), which copes with the dynamic feature of SGINs. We further theoretically verify the bounded regret of the PPSPS algorithm, indicating that it can ensure good semantic communication QoS. Simulation results demonstrate the superiority of the proposed path selection scheme compared to traditional reinforcement learning methods.