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English(EN) SLA-Safe Energy Control for AI-Native NG-RAN Using Stability-Aware Constrained PPO

AI框架在保持服务水平的同时降低5G能耗

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个新的由AI驱动的5G网络节能框架，该框架可确保服务水平协议（SLA）得到维持。该系统使用一种稳定性感知的约束强化学习方法，特别是约束近端策略优化（PPO），来动态管理无线资源和基站功率模式。在七个小区的环境中进行的模拟显示，在名义流量下，能耗显著降低了高达41.4%，同时在压力和未知流量条件下也保持了零SLA违规和吞吐量损失。 AI

影响 这项研究通过在不影响用户体验的情况下降低能耗，有望实现更高效、更具成本效益的5G网络运营。

排序理由 该集群包含一篇学术论文，详细介绍了一种针对特定技术问题的创新AI方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

AI框架在保持服务水平的同时降低5G能耗

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该集群包含一篇学术论文，详细介绍了一种针对特定技术问题的创新AI方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Dharmendra Kumar ·

    SLA-Safe Energy Control for AI-Native NG-RAN Using Stability-Aware Constrained PPO

    arXiv:2609.05861v1 Announce Type: cross Abstract: 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. Howev…

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