Predictive Impact Analysis for Designing a Resilient Cellular Backhaul Network

Abstract

Backhaul transport network design and optimization for cellular service providers involve a unique challenge stemming from the fact that an end-user's equipment (UE) is within the radio reach of multiple cellular towers: It is hard to evaluate the impact of the failure of the UE's primary serving tower on the UE, because the UE may simply switch to get service from other nearby cellular towers. To overcome this challenge, one needs to quantify the cellular service redundancy among the cellular towers riding on that transport circuit and their nearby cellular towers, which in turn requires a comprehensive understanding of the radio signal profile in the area of the impacted towers, the spatial distribution of UEs therein, and their expected workload (e.g., calls, data throughput). In this work, we develop a novel methodology for assessing the service impact of any hypothetical cellular tower outage scenario, and implement it in an operational system named Tower Outage Impact Predictor (TOIP). Our evaluations, using both synthetic data and historical real tower outages in a large operational cellular network, show conclusively that TOIP gives an accurate assessment of various tower outage scenarios, and can provide critical input data towards designing a reliable cellular backhaul transport network.