Wireless Propagation Model Tuning With Crowdsourced Coverage Feedback
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Solution Overview
Problem
Existing wireless network coverage maps are often outdated and inaccurate due to changes in terrain and clutter, such as buildings and vegetation, which are not effectively captured by nominal propagation models.
Innovation Solution
A system that collects geographical data, drive test data, and crowdsourced data to continuously tune and calibrate wireless network propagation models, using machine learning to adjust parameters based on real-time environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a nominal propagation model is used to represent wireless network coverage, then the model provides a basic prediction of coverage areas, but the model becomes outdated and inaccurate over time due to environmental changes such as terrain and clutter modifications
Solution Approach 1:
The system implements feedback by collecting actual drive test measurements and crowdsourced data from mobile devices, then using this feedback to continuously tune and update the propagation model parameters. This closed-loop approach ensures the model remains accurate despite environmental changes without requiring complete model regeneration
Solution Approach 2:
The propagation model transitions from a static nominal model to a dynamic model that continuously adapts to environmental changes. The system dynamically updates model parameters based on incoming measurement data, allowing the coverage predictions to evolve with terrain and clutter modifications over time
2Measurement precision
If drive test data and crowdsourced data are collected to tune the propagation model, then the model accuracy is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
Mobile devices serve multiple functions: they act as both consumer electronics for users and as measurement instruments for network optimization. The same smartphone hardware collects both crowdsourced operational data and dedicated drive test measurements, eliminating the need for separate specialized equipment
Solution Approach 2:
The system uses existing mobile devices that users already carry to collect measurement data, rather than requiring dedicated measurement equipment. The devices leverage their own antennas and processors to perform measurements and initial processing, reducing the complexity of the centralized collection system
3Reliability
If continuous tuning of the propagation model is performed using real-time data, then the coverage map remains current and accurate, but the computational resources and processing time required increase
Solution Approach 1:
The system performs partial tuning by updating only the necessary propagation model parameters based on incoming data, rather than completely reprocessing the entire model. This selective approach reduces computational overhead while maintaining accuracy where it matters most
Solution Approach 2:
The system performs preliminary filtering and processing of measurement data at the edge devices before transmitting to the central system. This preliminary action reduces the volume and complexity of data requiring intensive processing, lowering overall computational energy consumption
Data Source
AI summary
The present disclosure is directed to methods and systems for tuning a wireless network propagation model. A propagation model system can generate a nominal propagation model of a coverage area using collected geographical data and network equipment information. The nominal propagation model can provide a representation of the wireless network and coverage areas. The propagation model system can continuously calibrate the generated propagation models with continuous wave data and crowdsourced data from user devices. The crowdsourced data can provide a real-time representation of the coverage capability in an area as the terrain and clutter in an area can change.