DDPG-Based Resource Management in Network Slicing for 5G-Advanced V2X Services

The evolution of 5G technology towards 5G-Advanced has introduced advanced vehicular applications with stringent Quality-of-Service (QoS) requirements. Addressing these demands necessitates intelligent resource management within the standard 3GPP network slicing framework. This paper proposes a novel resource management scheme leveraging a Deep Deterministic Policy Gradient (DDPG) algorithm implemented in the Network Slice Subnet Management Function (NSSMF). The scheme dynamically allocates resources to network slices based on real-time traffic demands while maintaining compatibility with existing infrastructure, ensuring cost-effectiveness. The proposed framework features a two-level architecture: the gNodeB optimizes slice-level resource allocation at the upper level, and vehicles reserve resources dynamically at the lower level using the 3GPP Semi-Persistent Scheduling (SPS) mechanism. Evaluation in a realistic, trace-based vehicular environment demonstrates the scheme’s superiority over traditional approaches, achieving higher Packet Delivery Ratio (PDR), improved Spectral Efficiency (SE), and adaptability under varying vehicular densities. These results underscore the potential of the proposed solution in meeting the QoS demands of critical 5G-Advanced vehicular applications.