AI-Based Swapped Antenna Sector Detection
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Solution Overview
Problem
Current methods for detecting swapped antenna sectors in cellular communication networks are inefficient, inaccurate, and costly, relying on engineer expertise, time-consuming walk and drive tests, and high computational demands, with existing solutions failing to reliably prevent or detect swapped sectors due to stochastic signal strength measurements and data processing challenges.
Innovation Solution
A method using artificial intelligence, specifically a deep neural network, to predict antenna sector azimuths based on signal strength measurements and physical information, followed by a straightforward algorithm to detect swapped sectors, leveraging crowdsourced data, UE measurements, and walk and drive test data for geo-located signal strength inputs, allowing for flexible and accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If prevention processes are used to detect swapped sectors before bringing sectors on-air, then detection can be performed early, but the processes are not faultless and rely on engineer expertise which leads to inaccurate detection
Solution Approach 1:
The patent replaces manual engineer expertise and mechanical prevention processes with an automated AI-based detection system. The system uses machine learning models to analyze network data and automatically detect swapped sectors, eliminating the need for subjective engineer judgment while maintaining early detection capability.
Solution Approach 2:
The detection system operates autonomously by automatically collecting data from the network, processing it through AI algorithms, and generating detection results without requiring continuous human intervention. The system serves itself by maintaining and updating its own detection capabilities through ongoing data analysis.
2Measurement precision
If walk and drive testing methods are used to detect swapped sectors, then detection accuracy is very high, but the time of execution and associated costs make them very inefficient
Solution Approach 1:
The patent replaces physical walk and drive testing with an automated computational system that analyzes network data remotely. This substitution eliminates the need for physical movement and manual testing while maintaining or improving detection accuracy through AI-based analysis of signal patterns and network performance data.
Solution Approach 2:
Instead of performing actual physical walk and drive tests, the system creates virtual representations of network conditions by analyzing collected signal data and simulation results. The AI model processes copies of network data to predict swapped sectors without requiring physical presence at test locations.
3Ease of manufacture
If methods based on network mobility statistics are used to detect swapped sectors, then cost is significantly reduced, but the predictions are based on inaccurate information since they use estimation of user position instead of real position
Solution Approach 1:
The patent replaces position estimation methods with AI-based analysis that processes multiple data sources including signal strength, timing information, and network topology. The system substitutes inaccurate position estimates with more accurate position deductions derived from analyzing patterns in network data and signal characteristics.
Solution Approach 2:
The detection system combines multiple data sources and analysis methods to create a composite detection approach. By integrating information from various network measurements and AI algorithms, the system achieves both cost-effectiveness and improved position accuracy that neither method could achieve alone.
4Ease of manufacture
If method based on interference measurements is used to detect swapped sectors, then cost is significantly reduced compared with walk and drive tests, but the stochastic nature of signal strength measurements due to lack of geo-location and high processing capacity requirements remain drawbacks
Solution Approach 1:
The patent replaces direct signal strength measurement analysis with AI-based pattern recognition that processes interference data in conjunction with network topology and position information. This substitution reduces the impact of stochastic variations by analyzing patterns across multiple measurements and data sources rather than relying on individual signal strength readings.
Data Source
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AI summary
A method for detecting swapped antenna sectors in a cellular communications network. For each of one or more cells in the cellular communications network, an azimuth is estimated for each of two or more antenna sectors in the cell using a plurality of geo-located signal measurements for each antenna sector and a machine-learning algorithm. The estimated azimuths are compared to azimuths associated with the corresponding antenna sectors in a stored representation of the cellular communications network, to detect swapped antenna sectors in the cell.