Tower Outage Impact Predictor for Cellular Backhaul Resilience
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Solution Overview
Problem
Current technologies face challenges in accurately predicting the service impact of cellular tower outages in next-generation wireless communication networks, leading to inefficient design of resilient backhaul networks, as existing methods fail to account for the varying spatial distribution of mobile devices and radio signal profiles effectively.
Innovation Solution
A three-stage methodology is introduced, involving radio signal profiling, calibrating grid-level UE numbers using ridge regression, and survival analysis to predict the service impact of tower outages, allowing for optimal rehoming of cellular towers to minimize service disruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional outage prediction methods are used, then the design process is simple, but the prediction accuracy of service impact is low
Solution Approach 1:
The service area is divided into multiple grid sections, and the network is segmented into backhaul network segments. This segmentation allows for localized analysis of mobile device distribution and radio signal profiles in each grid section, enabling accurate prediction of service impact for specific outage scenarios without requiring complex city-wide modeling.
Solution Approach 2:
Radio signal profiles are constructed in advance for each grid section based on historical measurement data. Mobile device distribution patterns are pre-analyzed and stored. When an outage occurs, the system quickly queries pre-computed profiles and distributions to predict impact, avoiding the need for complex real-time calculations during actual outages.
2Measurement precision
If spatial distribution of mobile devices is not considered, then the analysis is straightforward, but the service impact prediction is inaccurate
Solution Approach 1:
The patent introduces a spatial dimension by dividing the service area into grid sections and constructing radio signal profiles that capture the spatial distribution of mobile devices. This dimensional transformation allows the system to analyze how outages affect different geographic areas differently, accurately reflecting that the same number of failed towers can have drastically different customer impact depending on their spatial locations and the device distribution in those areas.
3Measurement precision
If comprehensive radio signal profiling is performed, then the prediction accuracy improves, but the processing time increases
Solution Approach 1:
Radio signal profiles are constructed in advance during normal operation using historical measurement data from mobile devices. These profiles are stored and updated periodically. During outage prediction, the system simply queries the pre-computed profiles rather than performing new signal measurements and analysis, dramatically reducing processing time while maintaining high accuracy.
Solution Approach 2:
The system creates simplified copies of the actual radio environment through constructed radio signal profiles that capture essential characteristics without requiring full complexity of real-time signal analysis. These profile copies enable fast querying and comparison during outage scenarios.
Data Source
AI summary
Various embodiments disclosed herein provide for a tower outage impact predictor that can determine the service impact on an end user during a cellular tower outage. Radio signal profiling divides an area into grids, and then constructs a radio signal profile for each grid based on user equipment (UE) measurement data for each grid. The number of UEs in each grid is then determined, and then the number of UEs that lose service for a simulated cellular tower outage is determined. Based on the impact analysis, a more resilient backhaul network can be implemented by determining backhaul rehoming changes that optimally home cellular towers to various backhaul network devices.