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

arXiv: https://arxiv.org/abs/2512.22022 · HTML: https://mlsanity.com/paper/2512.22022/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 78 references that are in the map.
- First posted on arXiv 26 Dec 2025.

## Builds on (most cited first, 22 of 22)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
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- [Revisiting Smoothed Online Learning](https://mlsanity.com/paper/2102.06933/) (2021)
- [Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks](https://mlsanity.com/paper/2012.03158/) (2020)
- [Massive Access for 5G and Beyond](https://mlsanity.com/paper/2002.03491/) (2020)
- [Efficient Federated Meta-Learning over Multi-Access Wireless Networks](https://mlsanity.com/paper/2108.06453/) (2021)
- [Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems](https://mlsanity.com/paper/2210.02515/) (2022)
- [Online Convex Optimization for Caching Networks](https://mlsanity.com/paper/2001.06351/) (2020)
- [Embedding Model Based Fast Meta Learning for Downlink Beamforming Adaptation](https://mlsanity.com/paper/2109.09086/) (2021)
- [Surrogate "Level-Based" Lagrangian Relaxation for Mixed-Integer Linear Programming](https://mlsanity.com/paper/2203.04514/) (2022)
- [Forecaster-aided User Association and Load Balancing in Multi-band Mobile Networks](https://mlsanity.com/paper/2301.09294/) (2023)
- [Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning](https://mlsanity.com/paper/2309.01730/) (2023)
- [Smooth Handovers via Smoothed Online Learning](https://mlsanity.com/paper/2501.08099/) (2025)
- [On the Modeling and Analysis of Fast Conditional Handover for 5G-Advanced](https://mlsanity.com/paper/2204.06909/) (2022)
- [Conditional Handover in 5G: Principles, Future Use Cases and FR2 Performance](https://mlsanity.com/paper/2204.01283/) (2022)
- [On the Analysis and Optimization of Fast Conditional Handover with Hand Blockage for Mobility](https://mlsanity.com/paper/2211.00573/) (2022)
- [Conditional Handover Modelling for Increased Contention Free Resource Use in 5G-Advanced](https://mlsanity.com/paper/2307.14870/) (2023)
- [Mobility Performance Analysis of RACH Optimization Based on Decision Tree Supervised Learning for Conditional Handover in 5G Beamformed Networks](https://mlsanity.com/paper/2309.09840/) (2023)
- [Through the Telco Lens: A Countrywide Empirical Study of Cellular Handovers](https://mlsanity.com/paper/2411.19586/) (2024)
- [CHOMET: Conditional Handovers via Meta-Learning](https://mlsanity.com/paper/2507.07581/) (2025)

## Abstract (arXiv)

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.