Magus: minimizing cellular service disruption during network upgrades

Abstract

Planned upgrades in cellular networks occur every day, may often need to be performed on weekdays, and can potentially degrade service for customers. In this paper, we explore the problem of tuning network configurations in order to mitigate any potential impact due to a planned upgrade which takes the base station off-air. The objective is to recover the loss in service performance or coverage which would have occurred without any modifications. To our knowledge, impact mitigation for planned base station downtimes has not been explored before in the literature. The primary contribution of this work is a proactive approach based on a predictive model that uses operational data of user density distributions and path loss (rather than idealized analytical models of these) to quickly estimate the best power and tilt configuration of neighboring base stations that enables high recovery. A secondary contribution is an approach to minimize synchronized handovers. These ideas, embodied in a capability called Magus, enables us to recover up to 76% of the potential performance loss due to planned upgrades in some cases for a large US mobile network, and this recovery varies as a function of base station density. Moreover, Magus is able to reduce synchronized handovers by a factor of 8.