MADDPG-Based Deployment Algorithm for 5G Network Slicing

Abstract

One of the core features of 5G networks is the ability to support multiple services on the same infrastructure, with network slicing being a key technology. However, existing network slicing architectures have limitations in efficiently handling slice requests with different requirements, particularly when addressing high-reliability and high-demand services, where many issues remain unresolved. For example, predicting whether actual physical resources can meet network slice request demands and achieving flexible, on-demand resource allocation for different types of slice requests are significant challenges. To address the need for more flexible and efficient service demands, this paper proposes a 5G network slicing deployment algorithm based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Firstly, a new 5G network slicing deployment system framework is established, which measures resources for three typical 5G network slicing scenarios (eMBB, mMTC, uRLLC) and processes different types of slice requests by predicting slice request traffic. Secondly, by adopting the multi-agent approach of MADDPG, the algorithm enhances cooperation between multiple service requests, decentralizes action selection for requests, and schedules resources separately for the three types of slice requests, thereby optimizing resource allocation. Finally, simulation results demonstrate that the proposed algorithm significantly outperforms existing algorithms in terms of resource efficiency and slice request acceptance rate, showcasing the advantages of multi-agent approaches in slice request handling.