CHOMET: Conditional Handovers via Meta-Learning

2025-07-10

AI Summary

Conditional handover (CHO) in modern cellular networks causes inefficient resource allocation and excessive signaling overhead. Method: This paper proposes the first meta-learning-based dynamic optimization framework, tightly integrated with the O-RAN architecture, enabling millisecond-level adaptation to rapidly time-varying wireless channels. It online predicts the optimal set of target cells and jointly optimizes resource reservation and release policies. Contribution/Results: Theoretically, it provides a robust dynamic regret bound. Experimentally, under severe signal fluctuations, it achieves ≥180% improvement in handover success rate, significantly reducing handover failure probability and end-to-end latency—outperforming 3GPP baseline schemes.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Optimizing conditional handovers in complex cellular networks
• Reducing delays and failures in handover procedures
• Managing resource allocation and signaling overhead efficiently

Innovation

• Meta-learning optimizes conditional handovers
• O-RAN framework reduces signaling overhead
• Dynamic regret guarantees robust performance