DRL-Based Dynamic Resource Configuration and Optimization for B5G Network Slicing

Abstract

To effectively address the heterogeneous service requirements of diverse users, the Fifth Generation (5G) has introduced network slicing technology, which partitions physical resources into multiple virtual end-to-end network slices, allowing for the dynamic allocation of these slices. However, integrating network slicing with the 5G New Radio (NR) frame structure and allocating resources to meet Quality of Service (QoS) requirements, while ensuring user fairness, remains a challenging task. To tackle this challenge, a double layer resource configuration framework is proposed based on the 5G NR standard. Subsequently, an optimization problem is formulated to maximize user satisfaction taking into account their distinctive QoS requirements. In the upper layer of the framework, we leverage the Dueling Double Deep Q Network (D3QN) algorithm to optimize resource allocation from the base station to the network slices. In the lower layer, we employ the parameter tuning method to optimize the resource allocation of the users within each slice. These two steps are combined to form the Double Layer Distribution-D3QN (DLD-D3QN) algorithm. The effectiveness and superiority of the proposed algorithm is demonstrated through extensive simulations.

Keywords

• Deep reinforcement learning
• network slicing
• resource allocation
• 5G new radio