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Supervised QoS-Aware Energy Saving in O-RAN
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DOI：10.1109/lcomm.2026.3705559.png)
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Abstract
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This letter proposes a supervised solution deployed in a rApp that optimizes cell switch-on/off decisions to maximize energy efficiency in Open Radio Access Network (O-RAN) networks while guaranteeing Quality of Service (QoS) regarding outage probability. To address the lack of labeled data, we develop a local optimal solution leveraging Gaussian approximation and the Dinkelbach method. Validated in an environment aligned with O-RAN Alliance specifications, the proposed supervised model performs closely to the local optimal solution, achieving a gain of 98% during low-demand periods while strictly maintaining QoS requirements compared to the benchmark.
Keywords:
Open RAN
energy efficiency
artificial intelligence
supervised learning
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