Enhancing 5G Network Slicing: Slice Isolation Via Actor-Critic Reinforcement Learning with Optimal Graph Features

Abstract

Network slicing within 5G networks encounters two significant challenges: catering to a maximum number of requests while ensuring slice isolation. To address these challenges, we present an innovative actor-critic Reinforcement Learning (RL) model named 'Slice Isolation based on RL' (SIRL). This model employs five optimal graph features to construct the problem environment, the structure of which is adapted using a ranking scheme. This scheme effectively reduces feature dimensionality and enhances learning performance. SIRL was assessed through a comparative analysis with nine state-of-the-art RL models, utilizing four evaluation metrics. The average results demonstrate that SIRL outperforms other models with a 70% higher coverage rate of requests and an 8% reduction in damage resulting from DoS/DDoS attacks.