mlsanityData to 14 Oct 2026

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

arXiv 2504.19499 ↗First posted 28 Apr 2025cs.AI cs.IT cs.LGcited by 2

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

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.

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.

Builds on

7 of 17 references.

References · 7

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  3. 2019A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problemscited by 160
  4. 2023Artificial Intelligence in 3GPP 5G-Advanced: A Surveycited by 8
  5. 2019Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approachcited by 7
  6. 2021QoS-Aware Load Balancing in Wireless Networks using Clipped Double Q-Learningcited by 4
  7. 2024Probabilistic Mobility Load Balancing for Multi-band 5G and Beyond Networkscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Deep Reinforcement Learning-based Cell DTX/DRX Configuration for Network Energy Savingcited by 0
  2. 2026Place, Slice and Schedule: Hierarchical O-RAN Control of a Tethered mmWave UAV-gNBcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025QoS-Aware Dynamic CU Selection in O-RAN with Graph-Based Reinforcement Learning
  2. 2025Deep Reinforcement Learning Approach to QoSAware Load Balancing in 5G Cellular Networks under User Mobility and Observation Uncertainty
  3. 2025Near-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning
  4. 2025GCN-Driven Reinforcement Learning for Probabilistic Real-Time Guarantees in Industrial URLLC
  5. 2024Statistical QoS Provision in Business-Centric Networks
  6. 2025Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks
  7. 2025Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning
  8. 2025Towards Scalable O-RAN Resource Management: Graph-Augmented Proximal Policy Optimization