Research Paper#Mobile Networks, O-RAN, Meta-Learning, Handover Management🔬 ResearchAnalyzed: Jan 3, 2026 16:34
Meta-Learning for Handover Management in 5G/6G Networks
Published:Dec 26, 2025 13:01
•1 min read
•ArXiv
Analysis
This paper addresses the critical challenge of handover management in next-generation mobile networks, particularly focusing on the limitations of traditional and conditional handovers. The use of real-world, countrywide mobility datasets from a top-tier MNO provides a strong foundation for the proposed solution. The introduction of CONTRA, a meta-learning-based framework, is a significant contribution, offering a novel approach to jointly optimize THOs and CHOs within the O-RAN architecture. The paper's focus on near-real-time deployment as an O-RAN xApp and alignment with 6G goals further enhances its relevance. The evaluation results, demonstrating improved user throughput and reduced switching costs compared to baselines, validate the effectiveness of the proposed approach.
Key Takeaways
- •Proposes CONTRA, a meta-learning framework for joint optimization of THOs and CHOs in O-RAN.
- •Leverages real-world mobility datasets for training and evaluation.
- •Demonstrates improved user throughput and reduced switching costs compared to baselines.
- •Designed for near-real-time deployment as an O-RAN xApp.
Reference
“CONTRA improves user throughput and reduces both THO and CHO switching costs, outperforming 3GPP-compliant and Reinforcement Learning (RL) baselines in dynamic and real-world scenarios.”