Smart and connected mobility systems rely on 5G edge infrastructure to support real-time communication, control, and service differentiation. Achieving this requires adaptive resource management mechanisms that can react to rapidly changing traffic conditions. In this paper, we propose RL-Loop, a closed-loop reinforcement learning framework for real-time CPU resource control in 5G network slicing environments supporting connected mobility services. RL-Loop employs a Proximal Policy Optimization (PPO) agent that continuously observes slice-level key performance indicators and adjusts edge CPU allocations at one-second granularity on a real testbed. The framework leverages real-time observability and feedback to enable adaptive, software-defined edge intelligence. Experimental results suggest that RL-Loop can reduce average CPU allocation by over 55% relative to the reference operating point while reaching a comparable quality-of-service degradation region. These results indicate that lightweight reinforcement learning--based feedback control can provide efficient and responsive resource management for 5G-enabled smart mobility and connected vehicle services.
Real-World Deployment of Cloud-based Autonomous Mobility Systems for Outdoor and Indoor Environments
Department of Mechanical and Mechatronics Engineering, University of Waterloo（滑铁卢大学机械与机电工程系）；Technology Partnerships and Innovations, Rogers Communications, Canada Inc.（罗杰斯通讯加拿大有限公司技术伙伴关系与创新部）；Mechanical Engineering Department, University of Alberta（阿尔伯塔大学机械工程系）
Privacy-Aware Agent Collaboration for Dynamic VR Slice Management in 6G SD-RAN
CCAS Department, School of Engineering, NewGiza University (NGU)（计算机与软件工程学院，新吉扎大学）；Electronics and Communications, Helwan Univ. Cairo, Egypt（电子与通信，开罗海威大学，埃及）
