Digital Twin-Based Network Slice Optimization Using Deep Reinforcement Learning for Next-Generation Mobile Networks
Keywords:
digital twin, network slicing, deep reinforcement learning, 6G, mobile networks, resource allocation, QoS, sustainability, robustnessAbstract
Next-generation mobile networks require dynamic and fine-grained resource management to accommodate highly diverse service requirements under stringent performance constraints. Network slicing, underpinned by digital twin technology and deep reinforcement learning, presents a transformative paradigm for autonomous slice optimization at scale. This paper presents a comprehensive system-level examination of digital twin-based network slice optimization using deep reinforcement learning, focusing on architectural design, deployment challenges, governance, sustainability, robustness, and fairness. We propose a closed-loop control architecture in which a high-fidelity digital twin continuously mirrors the physical radio access and core network infrastructure, enabling proactive, conflict-free resource allocation. The twin’s predictive capabilities are combined with a centralized policy learning agent that uses proximal policy optimization to resolve the complex combinatorial resource allocation problem while respecting strict service-level agreements. Through an analysis of system integration, edge-cloud orchestration, and twin synchronization latency, the paper delineates the structural trade-offs between real-time responsiveness and modeling fidelity. Further, we discuss multi-tenant governance, regulatory implications, and the role of digital twins in ensuring transparent, auditable slice management. The study addresses robustness against twin model drift and adversarial perturbations, as well as fairness criteria that prevent resource starvation of lower-priority slices. An extended discussion covers energy footprint accounting, lifecycle carbon costs, and the alignment of slice optimization with sustainable networking initiatives. By synthesizing cross-domain insights from communications, artificial intelligence, distributed systems, and public policy, this work offers a forward-looking roadmap for deploying responsible, resilient, and equitable digital twin-driven network slicing platforms in 5G-Advanced and 6G ecosystems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
