用元学习优化蜂窝网络切换，提升成功率并降低延迟。
CHOMET: Conditional Handovers via Meta-Learning
- 基于元学习动态优化多小区准备资源分配
- 在信号波动环境下性能比3GPP基准高180%以上
- 适合需要低延迟高可靠切换的5G/6G网络场景
切换（HO）是现代蜂窝网络实现海量移动用户无缝连接的核心。随着网络复杂度提升、用户多样性和小小区增多，传统切换面临延迟长、失败率高等挑战。为此，3GPP引入了条件切换（CHO），通过为单个用户提前准备多个小区的资源，提高切换成功率并减少延迟。然而，CHO也带来了资源分配效率和频繁信令开销等新问题。本文提出一种符合O-RAN范式的新型框架，利用元学习优化CHO，提供稳健的动态后悔保证，并在信号波动条件下性能优于其他3GPP基准至少180%。
原文摘要 · Abstract (English)
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.
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