Smooth Handovers via Smoothed Online Learning

2025-01-14

AI Summary

Handovers across base stations in 5G/6G cellular networks suffer from instability, high failure rates, and excessive latency. Method: This paper proposes the first nationwide operational-network-oriented seamless handover optimization framework. It models UE–cell dynamic associations via Smooth Online Learning (SOL), jointly leveraging heterogeneous features from both UEs and base stations—without requiring channel prediction or prior mobility trajectory knowledge. The approach introduces SOL to large-scale commercial handover optimization for the first time, eliminating reliance on restrictive signal- or mobility-based assumptions; it further designs an O-RAN-compatible algorithm with provable dynamic regret guarantees. Results: Evaluated on a live European operator network serving over 40 million users, the method significantly reduces handover failure rate and latency. It consistently outperforms state-of-the-art baselines under both real-world and synthetic scenarios.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

With users demanding seamless connectivity, handovers (HOs) have become a fundamental element of cellular networks. However, optimizing HOs is a challenging problem, further exacerbated by the growing complexity of mobile networks. This paper presents the first countrywide study of HO optimization, through the prism of Smoothed Online Learning (SOL). We first analyze an extensive dataset from a commercial mobile network operator (MNO) in Europe with more than 40M users, to understand and reveal important features and performance impacts on HOs. Our findings highlight a correlation between HO failures/delays, and the characteristics of radio cells and end-user devices, showcasing the impact of heterogeneity in mobile networks nowadays. We subsequently model UE-cell associations as dynamic decisions and propose a realistic system model for smooth and accurate HOs that extends existing approaches by (i) incorporating device and cell features on HO optimization, and (ii) eliminating (prior) strong assumptions about requiring future signal measurements and knowledge of end-user mobility. Our algorithm, aligned with the O-RAN paradigm, provides robust dynamic regret guarantees, even in challenging environments, and shows superior performance in multiple scenarios with real-world and synthetic data.

Problem

• Signal Handover
• Cellular Network
• Network Optimization

Innovation

• Smooth Online Learning
• Signal Handover Optimization
• O-RAN Standard Compliance