Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing

TL;DR

This paper introduces a digital twin-enhanced deep reinforcement learning framework for network slicing resource management, enabling efficient, accurate, and scalable slice optimization without extensive real environment interaction.

Contribution

The paper presents a novel digital twin framework that calibrates with real data to improve DRL-based resource management in network slicing, including offline learning extensions.

Findings

Digital twin significantly improves slice optimization performance.

The framework demonstrates scalability and generalization via loss landscape analysis.

Offline reinforcement learning solutions are effective with historical data.

Abstract

Network slicing-based communication systems can dynamically and efficiently allocate resources for diversified services. However, due to the limitation of the network interface on channel access and the complexity of the resource allocation, it is challenging to achieve an acceptable solution in the practical system without precise prior knowledge of the dynamics probability model of the service requests. Existing work attempts to solve this problem using deep reinforcement learning (DRL), however, such methods usually require a lot of interaction with the real environment in order to achieve good results. In this paper, a framework consisting of a digital twin and reinforcement learning agents is present to handle the issue. Specifically, we propose to use the historical data and the neural networks to build a digital twin model to simulate the state variation law of the real environment. Then, we use the data generated by the network slicing environment to calibrate the digital twin so that it is in sync with the real environment. Finally, DRL for slice optimization optimizes its own performance in this virtual pre-verification environment. We conducted an exhaustive verification of the proposed digital twin framework to confirm its scalability. Specifically, we propose to use loss landscapes to visualize the generalization of DRL solutions. We explore a distillation-based optimization scheme for lightweight slicing strategies. In addition, we also extend the framework to offline reinforcement learning, where solutions can be used to obtain intelligent decisions based solely on historical data. Numerical simulation experiments show that the proposed digital twin can significantly improve the performance of the slice optimization strategy.