SLA-Safe Energy Control for AI-Native NG-RAN Using Stability-Aware Constrained PPO
TL;DR
Results show that stability-aware constrained reinforcement learning can provide a balanced AI-native RAN control approach for energy efficiency, SLA safety, robustness under traffic shift, and operational stability.
Abstract
Artificial intelligence is becoming a key enabler for AI-native radio access networks, where closed-loop learningbased controllers can support adaptive and autonomous network optimization. One important AI-for-RAN use case is energy saving, in which radio resources and cell energy modes must be dynamically controlled without violating user quality-of-service (QoS) or service-level agreement (SLA) requirements. However, aggressive sleep-state or deactivation decisions may reduce energy consumption at the cost of throughput degradation, delay increase, SLA violations, and unstable mode switching, especially under time-varying and bursty traffic conditions. This paper proposes a stability-aware constrained reinforcement learning framework for SLA-safe energy control in 5G NG-RAN. The problem is formulated as a constrained Markov decision process in which an AI-native controller selects closed-loop energy-saving actions based on cell load, queue status, active-user information, current energy mode, and SLA-related indicators. The proposed framework uses constrained proximal policy optimization with adaptive Lagrangian penalties to account for throughput, delay, and SLA constraints. To improve operation under traffic distribution shift, the controller is trained using mixed nominal and stress traffic regimes, while a switching-stability penalty is introduced to reduce oscillatory transitions between active and low-power modes. Simulation results in a seven-cell NG-RAN environment show that the proposed controller reduces energy consumption by approximately 41.4% under nominal traffic, 10.5% under stress traffic, and $22.9 \%$ under unseen-stress traffic relative to the Always-On baseline. Under stress and unseen-stress traffic, the controller preserves zero SLA violation and zero throughput loss, indicating service-preserving operation under challenging conditions. The proposed method also reduces switching activity compared with basic threshold-based energy saving. These results show that stability-aware constrained reinforcement learning can provide a balanced AI-native RAN control approach for energy efficiency, SLA safety, robustness under traffic shift, and operational stability.
