Geolocation Data Prioritisation for Wireless Fault Detection
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently locating faults due to the vast volume of data generated, leading to delayed fault detection and inadequate diagnostic detail, as conventional methods either store all data, rely on sampling, or conduct time-consuming drive tests, which are often ineffective in identifying issues within buildings or intermittent faults.
Innovation Solution
A method that processes all communication session data from multiple sectors in real-time, creating a prioritized data stream to produce geolocation data, allowing for near real-time fault detection and immediate accessibility of high priority and geolocation data, reducing the need for batch processing and sector-specific probing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all communication session data is stored using expensive storage technology, then complete fault diagnosis information is available, but storage cost and data access time increase significantly
Solution Approach 1:
The system extracts only the most relevant and frequently accessed communication session data elements needed for fault diagnosis, rather than storing all generated data. This selective extraction reduces storage requirements while maintaining diagnostic capability.
Solution Approach 2:
Different data retention strategies are applied to different types of communication session data based on their diagnostic value. High-priority data elements are retained longer and with higher fidelity, while less critical data is aggregated or discarded, creating differentiated data quality across the dataset.
2Quantity of substance
If data is captured for a limited part of the network for a limited time period using probes, then data volume is reduced, but fault detection capability and accuracy decrease
Solution Approach 1:
The network is divided into multiple sectors, and the system selectively monitors and captures communication session data from specific sectors based on fault reports and diagnostic needs. This segmented approach reduces overall data volume while maintaining the ability to detect faults in reported areas.
Solution Approach 2:
The system proactively captures and stores communication session data in advance based on fault reports before actual diagnosis is needed. This preliminary data capture ensures that relevant data is available when required, improving fault detection accuracy without continuously monitoring all network data.
3Power
If batch processing is used to analyze captured data, then processing resource requirements are reduced, but fault detection time increases significantly
Solution Approach 1:
The system implements periodic processing of communication session data at scheduled intervals, combining batch processing efficiency with timely fault detection. This periodic action ensures that data is processed regularly without requiring continuous high-resource allocation.
Solution Approach 2:
The system introduces an intermediary data structure that pre-processes and organizes communication session data before final analysis. This intermediary representation reduces the computational complexity of subsequent batch processing while maintaining diagnostic accuracy.
4Measurement precision
If expert staff are required to interpret results and perform additional calculations, then diagnostic accuracy is improved, but operational complexity and time delays increase
Solution Approach 1:
The system automatically performs geolocation calculations and fault analysis using pre-configured algorithms and data structures, eliminating the need for expert staff to perform manual calculations. The system serves itself by automatically interpreting captured communication session data and generating diagnostic results.
Solution Approach 2:
The system transforms raw communication session data into standardized parameters and formats that are directly suitable for automated analysis. By changing the parameter representation of the data, the system enables automatic interpretation without requiring expert manual intervention.
5Loss of information
If drive tests are conducted to diagnose faults, then field condition data is obtained, but testing time and resource requirements increase
Solution Approach 1:
The system captures communication session data that includes field condition information from actual network operations, creating a digital copy of field conditions without requiring physical drive tests. This data copying approach provides field condition data from real user equipment in actual service conditions.
Solution Approach 2:
The system proactively captures communication session data containing field condition information in advance, before drive tests would be required. This preliminary data capture from actual network operations eliminates the need for time-consuming physical testing.
Data Source
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AI summary
Communication session data from a mobile radio communications network (100) is processed to extract substantially all communication session data relating to calls in at least two sectors of the network. This processing occurs as the communication session data becomes available, thereby providing (510) a stream of communication session data (210). From the stream of communication session data (210), a high priority data stream (220) is created (520). The high priority data steam (220) comprises a minority of the communication session data for each callin the at least two sectors of the network (100). Geolocation data (230) is produced (530) for each call. An immediately accessible copy of both the high priority data stream (220) and the geolocation data (230) is provided (540), for each call.