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

2025-01-27

AI Summary

High energy consumption of Radio Units (RUs) in 5G Open RAN (O-RAN) architectures hinders green network deployment. Method: This paper proposes a lightweight, real-time deployable joint RU sleep-mode activation and radio resource scheduling framework. It introduces a dimension-agnostic encoder to ensure scalability across varying network sizes and establishes a single-actor–multi-critic reinforcement learning (RL) architecture, integrating distributional RL with Adaptive State Machine (ASM)-based control to jointly optimize dynamic network slicing under strict QoS constraints. Contribution/Results: Key innovations include (i) the first dimension-agnostic representation for O-RAN RL agents, (ii) multi-critic QoS decoupling for independent latency and throughput guarantees, and (iii) xApp-driven closed-loop data orchestration. Evaluated on commercial RUs and trajectory-driven simulations, the method reduces RU energy consumption by 15%–72% while strictly maintaining critical QoS metrics—including end-to-end latency and throughput—demonstrating strong operational readiness for live networks.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

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.

Problem

• 5G energy efficiency
• O-RAN architecture
• radio equipment power consumption

Innovation

• Kairos
• 5G Energy Efficiency
• O-RAN Architecture