CHOMET: Conditional Handovers via Meta-Learning

TL;DR

This paper introduces a meta-learning framework for conditional handovers in cellular networks, significantly improving success rates and reducing delays amid complex, dynamic environments.

Contribution

It proposes a novel meta-learning based approach for optimizing conditional handovers, addressing resource allocation and signaling challenges in modern cellular networks.

Findings

Achieves at least 180% performance improvement over benchmarks

Provides robust dynamic regret guarantees

Effective in volatile signal conditions

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.