Mohammed Imad Aal-Nouman, Sarmad M. Hadi and Haider A. H. Alobaidy

Federated Reinforcement Learning for Load Balancing in O-RAN 5G and Beyond Networks

The intelligent distributed control is a critical factor in next-generation mobile networks for connecting a massive number of devices and handling diverse services. The Open Radio Access Network (O-RAN) has recently gained significant traction as a flexible architecture based on standards that enables disaggregated, programmable network components. However, because O-RAN is inherently distributed, it creates an imbalance in traffic load when user mobility and traffic are dynamic. This paper proposes a Federated Reinforcement Learning (FedRL) framework to help load balance O- RAN units (O-RUs). The framework enables each O-RU to train a local reinforcement learning agent using real-time information on signal quality, mobility dynamics, and load conditions. The trained agents periodically share their learned information via federated averaging of their Q-tables at the Near-Real- Time RAN Intelligent Controller (Near- RT RIC), enabling collaborative learning without requiring centralized data collection. Using a synthetic dataset obtained from MATLAB-generated simulations of a mobile network of 500 m x 500 m, the proposed system is trained and tested. It simulates realistic user movement and radio conditions between multiple cells. In the experiments, FedRL reduces load imbalance by over 50% compared to rule-based schemes and by around 25% compared to centralized RL. It also achieves fairness values exceeding 0.95, increases throughput by more than 15%, increases handover success rates by around 13%, and reduces ping-pong events by 30-40 % compared to the baselines.

Reference:

DOI: 10.36244/ICJ.2026.2.6

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Please cite this paper the following way:

Mohammed Imad Aal-Nouman, Sarmad M. Hadi and Haider A. H. Alobaidy "Federated Reinforcement Learning for Load Balancing in O-RAN 5G and Beyond Networks", Infocommunications Journal, Vol. XVIII, No 2, June 2026, pp. 45-54., https://doi.org/10.36244/ICJ.2026.2.6