Improved Deep Reinforcement Learning With Faster Graph Recurrent Convolutional Neural Network‐Enabled Adaptive Network Slicing for Tailored Service Delivery in NextGen Networks

ABSTRACT Many new use cases and a broad spectrum of vertical businesses are expected to be supported by next‐generation wireless networks. Network slicing has been implemented to suit the stringent requirements of different services. By dividing the infrastructure network into several logical networks, this technology enables resource allocation based on services. In dynamic and unpredictable situations, managing resources across several domains and dimensions for end‐to‐end (E2E) slicing still presents difficulties. Tenant satisfaction requires striking a balance between the trade‐off between revenue and the expense of resource allocation. Network slicing has emerged as a fundamental paradigm in next‐generation networks to meet the diverse service requirements of various applications and users. However, the dynamic nature of network conditions and service demands poses challenges in efficiently allocating network resources to meet performance objectives. In this paper, a faster graph recurrent convolutional neural network (FGRCNN) with improved deep reinforcement learning (IDRL) is proposed to learn traffic behavior from link and node properties in addition to network structure. To train the FGRCNN model in the IDRL framework without requiring a labeled training dataset, employ the Deep Q‐learning technique. This allows the framework to swiftly adjust to changes in traffic dynamics. A system is proposed to analyze real‐time network and service data enabling dynamic adaptation of network slices for changing traffic patterns and service requirements. A comprehensive framework is presented that integrates deep learning models with network slicing orchestration mechanisms to achieve tailored service delivery. Through extensive simulations and experiments, the effectiveness approach in optimizing resource utilization is demonstrated, improving service quality and enabling agile network management in next‐gen networks. Results highlight the potential of deep learning‐enabled adaptive network slicing to support diverse and evolving service demands in future network environments.