Dynamic Modeling of Target Cell Location for Mobility Robustness Analysis in Cellular Networks: Technical Report
2026-08-03 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors studied how to best set handover (HO) parameters in cellular networks to avoid connection problems when a mobile user switches between base stations. They improved existing models by accurately describing where the next base station is located as the user moves in a straight line. Using this new model, they calculated the chances of two types of connection issues: switching too late and switching back and forth too quickly. Their results show that understanding the exact position of the target base station helps better predict these issues and find the best timing to switch connections.
handover (HO)time-to-trigger (TTT)ping-pong handovermobility robustness optimization (MRO)stochastic geometrybase station (BS)user equipment (UE)cellular networkssub-6 GHzspatial distribution
Authors
Kiichi Tokuyama
Abstract
Mobility robustness optimization (MRO) requires an appropriate selection of handover (HO) parameters such as the time-to-trigger (TTT) and the offset margin to balance HO failures and ping-pong HOs. Existing stochastic-geometry-based analyses for MRO often characterize the target base station (BS) by assuming that its angular position is uniformly distributed over a feasible region. However, this assumption does not explicitly capture the spatial distribution of the target BS dynamically selected as a user equipment (UE) moves through the network. In this paper, we develop a stochastic-geometry-based analytical framework for MRO in sub-6 GHz cellular networks. We first derive the distribution of the HO triggering time and the spatial distribution of the dynamically selected target BS under straight-line UE mobility. Based on these distributions, we formulate too-late HO and ping-pong HO events as mutually exclusive mobility events and analytically derive their probabilities. Numerical results validate the analytical expressions and demonstrate that explicitly characterizing the target BS distribution has a non-negligible impact on the evaluated HO performance. Furthermore, the derived framework quantifies the tradeoff between too-late HO and ping-pong HO probabilities with respect to the TTT and enables the identification of a TTT value that minimizes their sum.