Author ORCID Identifier

0000-0003-4969-0674

Document Type

Article

Disciplines

1.2 COMPUTER AND INFORMATION SCIENCE, 2. ENGINEERING AND TECHNOLOGY, 2.2 ELECTRICAL, ELECTRONIC, INFORMATION ENGINEERING

Publication Details

2025 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), 15-18 December 2025

DOI: 10.1109/ANTS66931.2025

Abstract

Open Radio Access Networks (Open RAN) provide

flexible, modular multi-vendor interoperability. Growing mobile

data demand requires balancing network performance with

power efficiency. Mobile operators need intelligent resource

management to achieve Key Performance Indicator (KPI) targets

while maintaining operational efficiency. This paper proposes

a solution using a multi-objective deep reinforcement learning

(MODRL) model deployed on the Open RAN Intelligent Con-

troller (RIC). Three customizable operator profiles (Power Sav-

ing, Balanced, and Performance) are used which define specific

priority ratios between performance and power saving objectives.

To evaluate, individual algorithms (CPU scheduling and UE

connection state switching) are implemented in Open RAN,

achieving 5–20%CPU power savings with bounded throughput

degradation observed during high-traffic scenarios when multiple

User Equipments (UEs) are connected. The MODRL model is

tested for successful selection between two algorithms across

three operator profiles for specific test scenarios. Performance

validation of throughput and power savings using MODRL

for algorithm selection in real-time network conditions with

additional test scenarios remain part of future work.

DOI

https://doi.org/10.1109/ANTS66931.2025

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

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