Cellular Spammer Location Detection Through Multi-Call Trace Aggregation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for locating spam callers using cellular networks are inaccurate due to short call durations and lack of mobility, leading to poor location estimation, especially in scenarios like E911 spam calls that overwhelm emergency services.
Innovation Solution
Aggregating data from multiple calls over a long time history and leveraging shared call features to narrow down the search area, using network measurements and external information, with optional deployment of mobile cells to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-call location methods are used, then system complexity is low, but location precision is poor due to short call durations and lack of mobility
Solution Approach 1:
The system performs preliminary actions by collecting and storing call trace records before location determination is needed. Call data is aggregated over time history, and network measurements are pre-collected from multiple calls, enabling accurate location estimation without requiring complex real-time processing during actual spam call detection.
Solution Approach 2:
The patent merges multiple call trace records and network measurements into a unified location estimation process. By combining data from multiple calls with different features (timing advance, cell information, RF measurements) and aggregating them over time history, the system achieves superior location precision that cannot be obtained from single-call analysis alone.
2Measurement precision
If multiple calls are aggregated over long time history, then location precision improves, but loss of time increases due to extended analysis period
Solution Approach 1:
Call trace records and network measurements are collected and stored in advance during normal network operation. This preliminary data collection means that when location determination is needed, the system can quickly analyze pre-aggregated data rather than collecting data in real-time, reducing the time loss associated with analyzing multiple calls.
Solution Approach 2:
The system analyzes only the necessary portions of aggregated call data required for location estimation, rather than processing every detail of all calls. By selectively extracting relevant features (timing advance, cell identifiers, RF measurements) from the aggregated data, the system achieves accurate location determination with reduced processing time.
3Measurement precision
If mobile cells are deployed to improve accuracy, then location detection precision increases, but device complexity and deployment cost increase
Solution Approach 1:
The patent introduces network measurements as an intermediary that bridges the gap between existing network infrastructure and location determination. By utilizing measurements already available from the cellular network (timing advance, cell information, RF measurements), the system achieves improved location accuracy without requiring additional mobile cell deployments or complex new infrastructure.
4Measurement precision
If call trace records are aggregated from multiple calls, then location precision improves, but quantity of data processing increases
Solution Approach 1:
The system extracts only the essential location-relevant features from the aggregated call trace records, such as timing advance values, cell identifiers, and RF measurements. By taking out only these critical data elements rather than processing the entire call record set, the system achieves accurate location estimation with reduced data processing volume.
Solution Approach 2:
The system transforms raw call data into location-specific parameters through aggregation and analysis. By changing the data representation from raw call traces to processed location parameters (derived from timing advance, cell geometry, and RF measurements), the system reduces the effective data quantity needed for location determination while maintaining or improving precision.
Data Source
AI summary
The described technology is generally directed towards spammer location detection, and in particular, to locating a spammer that makes multiple calls from a given location via a cellular communications network. In some examples, network equipment can obtain call trace records associated with the multiple calls, identify a group of call trace records based on a shared call trace feature, aggregate data from call trace records within the group, and determine an estimated location based on the aggregated data.