Intelligent Predictive Energy Management Of 5G Base Stations Using Reinforcement Learning Abstract

The aim of the paper is design of AI-based predictive energy management model for efficient wireless base stations using Reinforcement Learning (RL). The methodology employs a multi-phase methodology to develop and validate a reinforcement learning-based predictive energy management model for 5G base stations. Energy consumption data were collected and used to model the base station’s operation and formulate an optimization problem. A reinforcement learning algorithm was then implemented in Python to learn real-time energy-saving strategies based on traffic load and environmental factors. The model was validated through simulations and real-time tests, evaluating improvements in energy efficiency, service quality, and cost reduction. The testbed is MTN 5G site at Polo Park, Enugu. Findings from investigation revealed the cost of daily energy used by the site is ₦31377.7787 and the monthly running cost is ₦972711.1397. To reduce the economic burden, RL-based predictive energy management model was proposed as predictive and adaptive learning techniques which utilized Q- learning algorithm as the learning agent to adjust the action of the site based on dynamic state space of the network condition. Python programming language was used to train the model and also integration on the 5G network. Results obtained revealed that with RL, the daily energy saved when compared with characterized is ₦4625.0896. The monthly energy saved amount to ₦170130.4667 with RL-based predictive energy management model, while every year, ₦2041565.6004 is the amount saved. The reason was because of the ability of the RL to adjust the action of RL based on the different state space of the site to save energy. The percentage reduction in daily energy consumed with RL based station is 33.90%. The percentage reduction in monthly energy consumed is 14.74%. In conclusion the study has demonstrated the RL can help manage energy consumption in the 5G network while maintaining quality of service.