<ul data-eligibleForWebStory="false"><li>Handovers (HOs) are crucial in cellular networks for ensuring connectivity to mobile users, but traditional HOs face challenges in complex networks with diverse users and smaller cells.</li><li>To address these challenges, 3GPP introduced conditional handovers (CHOs) which prepare multiple cells for a single user, aiming to increase HO success rates and reduce delays.</li><li>However, CHOs bring new challenges like efficient resource allocation and managing signaling overhead, which require optimization.</li><li>A new framework utilizing meta-learning for CHO optimization within the O-RAN paradigm has shown significant performance improvements, outperforming 3GPP benchmarks by at least 180% in dynamic signal conditions.</li></ul>

CHOMET: Conditional Handovers via Meta-Learning

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