# Kairos: Energy-Efficient Radio Unit Control for O-RAN via Advanced Sleep Modes

arXiv: https://arxiv.org/abs/2501.15853 · HTML: https://mlsanity.com/paper/2501.15853/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2025.

## Built on by (most cited first, 4 of 4)

- [Goal-Oriented Time-Series Forecasting: Foundation Framework Design](https://mlsanity.com/paper/2504.17493/) (2025)
- [EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RAN](https://mlsanity.com/paper/2602.09206/) (2026)
- [Energy-Aware 6G Network Design: A Survey](https://mlsanity.com/paper/2509.11289/) (2025)
- [Energy Saving for Cell-Free Massive MIMO Networks: A Multi-Agent Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2604.07133/) (2026)

## Abstract (arXiv)

The high energy footprint of 5G base stations, particularly the radio units (RUs), poses a significant environmental and economic challenge. We introduce Kairos, a novel approach to maximize the energy-saving potential of O-RAN's Advanced Sleep Modes (ASMs). Unlike state-of-the-art solutions, which often rely on complex ASM selection algorithms unsuitable for time-constrained base stations and fail to guarantee stringent QoS demands, Kairos offers a simple yet effective joint ASM selection and radio scheduling policy capable of real-time operation. This policy is then optimized using a data-driven algorithm within an xApp, which enables several key innovations: (i) a dimensionality-invariant encoder to handle variable input sizes (e.g., time-varying network slices), (ii) distributional critics to accurately model QoS metrics and ensure constraint satisfaction, and (iii) a single-actor-multiple-critic architecture to effectively manage multiple constraints. Through experimental analysis on a commercial RU and trace-driven simulations, we demonstrate Kairos's potential to achieve energy reductions ranging between 15% and 72% while meeting QoS requirements, offering a practical solution for cost- and energy-efficient 5G networks.