Computation and Privacy Protection for Satellite-Ground Digital Twin Networks

TL;DR

This paper introduces a novel blockchain-aided game-theoretic and deep reinforcement learning framework to optimize computation, privacy, and resource management in satellite-ground digital twin networks, enhancing performance and privacy.

Contribution

It proposes a combined Stackelberg game model and MAML-MADFRL framework for joint optimization of pricing, resource allocation, and privacy in SGIDTNs, addressing key challenges.

Findings

MAML-MADFRL improves network throughput and privacy protection.

The framework reduces channel interference and increases cloud profits.

Simulation shows superiority over baseline methods.

Abstract

Satellite-ground integrated digital twin networks (SGIDTNs) are regarded as innovative network architectures for reducing network congestion, enabling nearly-instant data mapping from the physical world to digital systems, and offering ubiquitous intelligence services to terrestrial users. However, the 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, are critical for improving quality of service in SGIDTNs. Hence, we establish a blockchain-aided Stackelberg game model for maximizing the pricing profits and network throughput in terms of minimizing overhead of privacy protection, thus performing computation offloading, decreasing channel interference, and improving privacy protection. Next, we propose a Lyapunov stability theory-based model-agnostic metalearning aided multi-agent deep federated reinforcement learning (MAML-MADFRL) framework for optimizing the CPU cycle frequency, channel selection, task-offloading decision, block size, and cloud server price, which facilitate the integration of communication, computation, and block resources. Subsequently, the extensive performance analyses show that the proposed MAMLMADFRL algorithm can strengthen the privacy protection via the transaction verification mechanism, approach the optimal time average penalty, and fulfill the long-term average queue size via lower computational complexity. Finally, our simulation results indicate that the proposed MAML-MADFRL learning framework is superior to the existing baseline methods in terms of network throughput, channel interference, cloud server profits, and privacy overhead.