# Graph Reinforcement Learning for QoS-Aware Load Balancing in Open Radio Access Networks

arXiv: https://arxiv.org/abs/2504.19499 · HTML: https://mlsanity.com/paper/2504.19499/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 7 of 7)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Artificial Intelligence in 3GPP 5G-Advanced: A Survey](https://mlsanity.com/paper/2305.05092/) (2023)
- [Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach](https://mlsanity.com/paper/1906.00767/) (2019)
- [QoS-Aware Load Balancing in Wireless Networks using Clipped Double Q-Learning](https://mlsanity.com/paper/2107.04207/) (2021)
- [Probabilistic Mobility Load Balancing for Multi-band 5G and Beyond Networks](https://mlsanity.com/paper/2401.13792/) (2024)

## Built on by (most cited first, 2 of 2)

- [Deep Reinforcement Learning-based Cell DTX/DRX Configuration for Network Energy Saving](https://mlsanity.com/paper/2507.21385/) (2025)
- [Place, Slice and Schedule: Hierarchical O-RAN Control of a Tethered mmWave UAV-gNB](https://mlsanity.com/paper/2608.23824/) (2026)

## Abstract (arXiv)

Next-generation wireless cellular networks are expected to provide unparalleled Quality-of-Service (QoS) for emerging wireless applications, necessitating strict performance guarantees, e.g., in terms of link-level data rates. A critical challenge in meeting these QoS requirements is the prevention of cell congestion, which involves balancing the load to ensure sufficient radio resources are available for each cell to serve its designated User Equipments (UEs). In this work, a novel QoS-aware Load Balancing (LB) approach is developed to optimize the performance of Guaranteed Bit Rate (GBR) and Best Effort (BE) traffic in a multi-band Open Radio Access Network (O-RAN) under QoS and resource constraints. The proposed solution builds on Graph Reinforcement Learning (GRL), a powerful framework at the intersection of Graph Neural Network (GNN) and RL. The QoS-aware LB is modeled as a Markov Decision Process, with states represented as graphs. QoS consideration are integrated into both state representations and reward signal design. The LB agent is then trained using an off-policy dueling Deep Q Network (DQN) that leverages a GNN-based architecture. This design ensures the LB policy is invariant to the ordering of nodes (UE or cell), flexible in handling various network sizes, and capable of accounting for spatial node dependencies in LB decisions. Performance of the GRL-based solution is compared with two baseline methods. Results show substantial performance gains, including a 53% reduction in QoS violations and a fourfold increase in the 5th percentile rate for BE traffic.