CHOMET: Conditional Handovers via Meta-Learning
This work addresses efficiency and reliability issues in cellular networks for mobile users, representing an incremental improvement over existing CHO methods.
The paper tackles the challenges of conditional handovers (CHOs) in cellular networks, such as resource allocation and signaling overhead, by proposing a meta-learning framework that achieves at least 180% superior performance compared to 3GPP benchmarks in volatile signal conditions.
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.