A Learning Approach to Frequent Handover Mitigations in 3GPP Mobility Protocols

The industry standard 3GPP mobility solutions are analyzed through the lens of bandit learning theory. In particular, it is shown that the original 3GPP handover protocol, developed primarily from a radio frequency and load balancing perspective, can be viewed as a special case of the $\epsilon$-greedy bandit algorithm, and thus its sub- optimality can be characterized via the regret analysis. Inspired by the equivalence between 3GPP handover protocols and bandit algorithms, we rigorously analyze the performance of cell range expansion in 3GPP handover enhancement, and further propose a learning-based approach to address the frequent handover (FHO) challenges in ultra-dense networks. The key component is to explicitly consider the handover cost to discourage FHOs. Rather surprisingly, we prove that the bandit-inspired scheme with handover cost can be viewed as an enhancement to the simple sticky biasing solution in 3GPP that has been developed to partially address the FHO problem, and hence lay a theoretic foundation to this industrial intuition.