Multi-Objective Deep Reinforcement Learning for Dynamic Algorithm Selection in Open RAN

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 Controller (RIC). Three customizable operator profiles (Power Saving, 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.