SLA-Aware Resource Allocation for V2X Network Slicing via SAC-based O-RAN xApp

Abstract

This paper proposes a Service Level Agreement (SLA)-aware resource allocation framework for 6G V2X slicing, realized as a Soft Actor-Critic (SAC) based xApp within the Open-Radio Access Network (O-RAN) near-realtime-RAN Intelligent Controller (near-RT-RIC). The xApp dynamically distributes radio resources across heterogeneous slices, minimizing SLA violations while considering fairness and throughput efficiency. Unlike heuristic or single-metric Deep Reinforcement Learning (DRL) methods, our design incorporates deadline awareness and service reliability directly into the reward formulation. Simulation results show that the proposed scheme consistently outperforms fixed, random, proportional, and Exponential moving Average (EMA)-based baselines, improving average packet delivery ratio (PDR), reducing mean SLA violations, and achieving a Pareto-optimal trade-off between throughput and compliance. These findings demonstrate the potential of O-RAN-native intelligent control for future 6G networks.