Wireless Propagation Model Tuning with Crowdsourced RF Data
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Solution Overview
Problem
Existing wireless network coverage maps provided by carriers are often outdated and inaccurate due to changes in terrain and clutter, such as buildings and vegetation, which affect signal quality and coverage.
Innovation Solution
A propagation model system that collects geographical data, uses continuous wave data from drive tests, and incorporates crowdsourced data from user devices to continuously tune and calibrate the network model, ensuring accurate representation of coverage areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a nominal propagation model is generated using geographical data and network equipment information, then a representation of the wireless network and coverage areas is provided, but the accuracy of the coverage representation deteriorates over time due to changes in terrain and clutter
Solution Approach 1:
The system collects crowdsourced RF signal measurement data from mobile devices throughout the coverage area and uses this feedback to continuously tune and recalibrate the propagation model parameters. This closed-loop feedback mechanism ensures the model remains accurate despite changes in terrain and clutter over time.
Solution Approach 2:
The propagation model transitions from a static representation to a dynamic one that continuously adapts to changing environmental conditions. The model parameters are regularly updated based on new crowdsourced data, allowing the system to reflect real-time changes in the wireless environment.
2Measurement precision
If crowdsourced data from user devices is collected to continuously tune the propagation model, then the accuracy and real-time representation of coverage capability is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
Mobile devices automatically collect and transmit RF signal measurement data without requiring user intervention. The devices serve themselves by utilizing their existing antennas and processors to gather propagation data, which is then automatically uploaded to the network for model tuning.
Solution Approach 2:
The system leverages the existing mobile devices and their communication capabilities for dual purposes: normal cellular communication and propagation model data collection. This multi-functional approach avoids the need for dedicated measurement equipment, reducing overall system complexity.
3Measurement precision
If continuous wave data from drive tests is used to tune the propagation model, then the initial model accuracy is improved, but the time and resources required for model calibration increase
Solution Approach 1:
The system performs preliminary model tuning using drive test data to establish an initial accurate propagation model before deploying crowdsourced data collection. This preliminary calibration ensures the model starts with high accuracy, reducing the time needed for subsequent refinements.
Solution Approach 2:
The propagation model tuning transitions from periodic drive tests to continuous crowdsourced data collection. Once the model is initially calibrated using drive test data, the system continuously refines it using data from mobile devices, eliminating the need for repeated time-consuming drive tests.
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.