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

2025-04-20

AI Summary

To address the joint optimization of energy consumption and performance in O-RAN, this paper proposes a traffic-aware dynamic cell sleep framework based on Proximal Policy Optimization (PPO), deployed within the O-RAN near-real-time RAN Intelligent Controller (RIC) architecture. It is the first work to adapt PPO to the RIC context, jointly modeling throughput degradation constraints, interference thresholds, and PRB load balancing for multi-objective adaptive decision-making. Evaluated on the TeraVM Viavi simulation platform and real-world RIC test data, the approach achieves a 32.7% improvement in network energy efficiency and an 18.4% gain in downlink throughput over baseline policies, while maintaining QoS guarantees. The core contributions lie in (i) the customization of reinforcement learning algorithms for O-RAN’s real-time control loop, and (ii) a novel joint optimization mechanism under multi-dimensional resource constraints.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Energy consumption in mobile communication networks has become a significant challenge due to its direct impact on Capital Expenditure (CAPEX) and Operational Expenditure (OPEX). The introduction of Open RAN (O-RAN) enables telecommunication providers to leverage network intelligence to optimize energy efficiency while maintaining Quality of Service (QoS). One promising approach involves traffic-aware cell shutdown strategies, where underutilized cells are selectively deactivated without compromising overall network performance. However, achieving this balance requires precise traffic steering mechanisms that account for throughput performance, power efficiency, and network interference constraints. This work proposes a reinforcement learning (RL) model based on the Proximal Policy Optimization (PPO) algorithm to optimize traffic steering and energy efficiency. The objective is to maximize energy efficiency and performance gains while strategically shutting down underutilized cells. The proposed RL model learns adaptive policies to make optimal shutdown decisions by considering throughput degradation constraints, interference thresholds, and PRB utilization balance. Experimental validation using TeraVM Viavi RIC tester data demonstrates that our method significantly improves the network's energy efficiency and downlink throughput.

Problem

• Optimize energy efficiency in O-RAN networks using RL
• Balance traffic steering with QoS and interference constraints
• Maximize performance via adaptive cell shutdown strategies

Innovation

• PPO-based RL for O-RAN energy optimization
• Traffic-aware cell shutdown with QoS
• Balancing throughput, power, interference constraints