Computation and Privacy Protection for Satellite-Ground Digital Twin Networks

Abstract

Satellite-ground integrated heterogeneous networks can relieve network congestion, release network resources and provide ubiquitous intelligence services for terrestrial users. Furthermore, digital twin technology can enable nearly-instant data mapping from the physical world to digital systems. The integration between satellite-ground integrated heterogeneous networks and digital twin alleviates the gap between data analyses and physical unities. However, the current challenges, such as the pricing policy, the stochastic task arrivals, the time-varying satellite locations, mutual channel interference, and resource scheduling mechanisms between the users and cloud servers, severely affect the improvement of quality of service. Hence, we establish a blockchain-aided Stackelberg game model for maximizing the pricing profits and network throughput in terms of minimizing privacy overhead, which is able to perform computation offloading, decrease channel interference, and improve privacy protection. Due to the long-term task queue in Stackelberg model, we propose a Lyapunov stability theory-based model-agnostic meta-learning aided multi-agent deep federated reinforcement learning framework to transfer the long-term task queue into the single time slot, and then optimize the central processing unit frequency, channel selection, task-offloading decision, block size, and cloud server price, which facilitate the integration of communication, computation, and block resources. Subsequently, several performance analyses show that the proposed learning framework can strengthen the privacy protection, approach the optimal time average function, and fulfill the long-term average queue size via lower computational complexity. Finally, our simulation results indicate that the proposed learning framework is superior to the existing baseline methods in terms of network throughput, channel interference, cloud server profits, and privacy overhead.