EcoEdgeTwin: Enhanced 6G Network via Mobile Edge Computing and Digital Twin Integration

TL;DR

EcoEdgeTwin tackles the challenge of delivering energy-efficient, low-latency MEC-enabled 6G networks under user mobility by integrating Digital Twins with MEC and guiding decisions through an Advantage Actor-Critic (A2C) reinforcement learning framework. The model jointly optimizes task offloading, service caching, and reliable migration while incorporating DT-predicted states and a DT discrepancy factor to refine latency and energy estimates. A QoE-centric utility balances user satisfaction with latency and cost, and the DRL solution learns policies from a state that fuses physical network metrics with DT insights. Simulation results on dense urban settings with DT-enhanced offloading demonstrate substantial improvements in energy consumption, latency, and QoE over DT-absent benchmarks, underscoring the practical value of DT-guided MEC in 6G deployments.

Abstract

In the 6G era, integrating Mobile Edge Computing (MEC) and Digital Twin (DT) technologies presents a transformative approach to enhance network performance through predictive, adaptive control for energy-efficient, low-latency communication. This paper presents the EcoEdgeTwin model, an innovative framework that harnesses the synergy between MEC and DT technologies to ensure efficient network operation. We optimize the utility function within the EcoEdgeTwin model to balance enhancing users' Quality of Experience (QoE) and minimizing latency and energy consumption at edge servers. This approach ensures efficient and adaptable network operations, utilizing DT to synchronize and integrate real-time data seamlessly. Our framework achieves this by implementing robust mechanisms for task offloading, service caching, and cost-effective service migration. Additionally, it manages energy consumption related to task processing, communication, and the influence of DT predictions, all essential for optimizing latency and minimizing energy usage. Through the utility model, we also prioritize QoE, fostering a user-centric approach to network management that balances network efficiency with user satisfaction. A cornerstone of our approach is integrating the advantage actor-critic algorithm, marking a pioneering use of deep reinforcement learning for dynamic network management. This strategy addresses challenges in service mobility and network variability, ensuring optimal network performance matrices. Our extensive simulations demonstrate that compared to benchmark models lacking DT integration, EcoEdgeTwin framework significantly reduces energy usage and latency while enhancing QoE.

Figures (3)

• Figure 1: EcoEdgeTwin model
• Figure 2: Performance metrics of EcoEdgeTwin model
• Figure 3: Comparison: EcoEdgeTwin vs. benchmark