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Enhancing Energy Efficiency in O-RAN Through Intelligent xApps Deployment

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arxiv 2405.10116 v1 pith:SFRKRL47 submitted 2024-05-16 eess.SY cs.SYeess.SP

classification eess.SYcs.SYeess.SP
keywords powerenergyintelligento-ranxappsarchitectureconditionsconsumption
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The proliferation of 5G technology presents an unprecedented challenge in managing the energy consumption of densely deployed network infrastructures, particularly Base Stations (BSs), which account for the majority of power usage in mobile networks. The O-RAN architecture, with its emphasis on open and intelligent design, offers a promising framework to address the Energy Efficiency (EE) demands of modern telecommunication systems. This paper introduces two xApps designed for the O-RAN architecture to optimize power savings without compromising the Quality of Service (QoS). Utilizing a commercial RAN Intelligent Controller (RIC) simulator, we demonstrate the effectiveness of our proposed xApps through extensive simulations that reflect real-world operational conditions. Our results show a significant reduction in power consumption, achieving up to 50% power savings with a minimal number of User Equipments (UEs), by intelligently managing the operational state of Radio Cards (RCs), particularly through switching between active and sleep modes based on network resource block usage conditions.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning

    cs.NI 2025-04 conditional novelty 4.0 of 10

    A PPO-based reinforcement learning policy for O-RAN cell shutdown and traffic steering achieves higher simulated energy efficiency and throughput than SARSA and random selection baselines.

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