Meta-Learning-Based Handover Management in NextG O-RAN

2025-12-26

AI Summary

To address the low reliability, high signaling overhead, and excessive latency of traditional handover (THO) and conditional handover (CHO) in NextG ultra-dense deployments and high-frequency bands, this paper proposes CONTRA—the first O-RAN-native joint dynamic THO/CHO decision-making framework. Its core innovation lies in: (i) a practical meta-learning algorithm tailored for runtime observations, enabling near-oracular, no-regret online decisions; and (ii) deep integration with the O-RAN xApp architecture and near-real-time RAN Intelligent Controller (RIC). Evaluated on nationwide real-world operator data, CONTRA significantly reduces total THO/CHO signaling overhead while increasing average user throughput. It consistently outperforms both 3GPP-standardized procedures and state-of-the-art reinforcement learning baselines across all key metrics, demonstrating superior adaptability, efficiency, and scalability in dynamic wireless environments.

Technology Category

• Search and Optimization: Algorithm Configuration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Online Learning & Bandits

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

While traditional handovers (THOs) have served as a backbone for mobile connectivity, they increasingly suffer from failures and delays, especially in dense deployments and high-frequency bands. To address these limitations, 3GPP introduced Conditional Handovers (CHOs) that enable proactive cell reservations and user-driven execution. However, both handover (HO) types present intricate trade-offs in signaling, resource usage, and reliability. This paper presents unique, countrywide mobility management datasets from a top-tier mobile network operator (MNO) that offer fresh insights into these issues and call for adaptive and robust HO control in next-generation networks. Motivated by these findings, we propose CONTRA, a framework that, for the first time, jointly optimizes THOs and CHOs within the O-RAN architecture. We study two variants of CONTRA: one where users are a priori assigned to one of the HO types, reflecting distinct service or user-specific requirements, as well as a more dynamic formulation where the controller decides on-the-fly the HO type, based on system conditions and needs. To this end, it relies on a practical meta-learning algorithm that adapts to runtime observations and guarantees performance comparable to an oracle with perfect future information (universal no-regret). CONTRA is specifically designed for near-real-time deployment as an O-RAN xApp and aligns with the 6G goals of flexible and intelligent control. Extensive evaluations leveraging crowdsourced datasets show that 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.

Problem

• Optimizes handover management in O-RAN to reduce failures and delays
• Jointly controls traditional and conditional handovers using meta-learning
• Enhances throughput and cuts switching costs in dynamic network conditions

Innovation

• Meta-learning algorithm adapts to runtime observations
• Jointly optimizes traditional and conditional handovers in O-RAN
• Dynamic on-the-fly handover type selection based on conditions