SLA安全的AI原生NG-RAN能量控制：基于稳定性感知的约束PPO
SLA-Safe Energy Control for AI-Native NG-RAN Using Stability-Aware Constrained PPO
- Independent Researcher Dallas, TX, USA
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对5G NG-RAN节能中的SLA安全问题，提出稳定性感知的约束PPO框架，通过混合流量训练和切换惩罚，在七小区仿真中实现最高41.4%能耗降低且零SLA违规。
AI中文摘要：
AI用于RAN的一个重要用例是节能，其中必须在不违反用户服务质量（QoS）或服务级别协议（SLA）要求的情况下，动态控制无线电资源和小区能量模式。然而，激进的休眠状态或去激活决策可能会以吞吐量下降、时延增加、SLA违规和模式切换不稳定为代价来降低能耗，尤其是在时变和突发流量条件下。本文提出了一种用于5G NG-RAN中SLA安全能量控制的稳定性感知约束强化学习框架。该问题被建模为约束马尔可夫决策过程，其中AI原生控制器基于小区负载、队列状态、活跃用户信息、当前能量模式和SLA相关指标来选择闭环节能动作。所提出的框架使用带有自适应拉格朗日惩罚的约束近端策略优化来处理吞吐量、时延和SLA约束。为了改善流量分布偏移下的运行，控制器使用混合的名义流量和压力流量机制进行训练，同时引入切换稳定性惩罚以减少活跃模式与低功耗模式之间的振荡转换。在七小区NG-RAN环境中的仿真结果表明，与Always-On基线相比，所提出的控制器在名义流量下能耗降低约41.4%，在压力流量下降低10.5%，在未见过的压力流量下降低22.9%。在压力流量和未见过的压力流量下，该控制器保持零SLA违规和零吞吐量损失，表明在具有挑战性的条件下实现了服务保持操作。所提出的方法还相比基于基本阈值的节能减少了切换活动。
英文摘要：
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.
