# CHOMET: Conditional Handovers via Meta-Learning

arXiv: https://arxiv.org/abs/2507.07581 · HTML: https://mlsanity.com/paper/2507.07581/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 17 of 17)

- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Adaptive Online Learning in Dynamic Environments](https://mlsanity.com/paper/1810.10815/) (2018)
- [Revisiting Smoothed Online Learning](https://mlsanity.com/paper/2102.06933/) (2021)
- [Energy-aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning](https://mlsanity.com/paper/2208.09956/) (2022)
- [Online Convex Optimization with Binary Constraints](https://mlsanity.com/paper/2005.02274/) (2020)
- [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)
- [Network Slicing to Enable Scalability and Flexibility in 5G Mobile Networks](https://mlsanity.com/paper/1704.02129/) (2017)
- [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)

## Built on by (most cited first, 1 of 1)

- [Meta-Learning-Based Handover Management in NextG O-RAN](https://mlsanity.com/paper/2512.22022/) (2025)

## Abstract (arXiv)

Handovers (HOs) are the cornerstone of modern cellular networks for enabling seamless connectivity to a vast and diverse number of mobile users. However, as mobile networks become more complex with more diverse users and smaller cells, traditional HOs face significant challenges, such as prolonged delays and increased failures. To mitigate these issues, 3GPP introduced conditional handovers (CHOs), a new type of HO that enables the preparation (i.e., resource allocation) of multiple cells for a single user to increase the chance of HO success and decrease the delays in the procedure. Despite its advantages, CHO introduces new challenges that must be addressed, including efficient resource allocation and managing signaling/communication overhead from frequent cell preparations and releases. This paper presents a novel framework aligned with the O-RAN paradigm that leverages meta-learning for CHO optimization, providing robust dynamic regret guarantees and demonstrating at least 180% superior performance than other 3GPP benchmarks in volatile signal conditions.