Radio Quality Estimation Using Weather and Geographic Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing radio quality estimation methods for mmWave bands fail to accurately account for geographical factors such as distance and topography between observation stations and radio base stations, leading to inaccuracies in predicting radio wave conditions, which is critical for high-reliability back-haul links.
Innovation Solution
A method that acquires and learns from both weather and geographic information at multiple observation points to estimate radio quality, using a model that incorporates weather and geographic data to predict radio wave conditions, including wind direction and speed, to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If weather information from observation stations is used to estimate radio quality, then the estimation can be performed using available weather data, but the accuracy deteriorates due to geographical factors such as distance and topography between observation stations and radio base stations
Solution Approach 1:
The patent applies local quality by training separate estimation models for different geographical regions. Each model is trained using weather information and radio quality data from observation stations within a specific region, allowing the model to capture local topographical and atmospheric characteristics. This enables accurate radio quality estimation that accounts for regional variations in distance, terrain, and weather patterns between observation stations and radio base stations.
2Measurement precision
If multiple observation points are used to improve estimation accuracy, then the prediction precision improves, but the system complexity increases due to data acquisition and model training requirements
Solution Approach 1:
The patent merges data from multiple observation points into a unified estimation model. The model integrates weather information and radio quality data from multiple observation stations within a region, combining these diverse data sources to improve prediction accuracy. This merging approach allows the system to leverage information from multiple locations without requiring separate complex systems for each observation point.
Solution Approach 2:
The system employs self-service through automated model training and updating. The estimation model is automatically trained using historical weather information and radio quality data from multiple observation points, and it continuously improves by learning from new data. This self-service mechanism reduces the need for manual system configuration and maintenance, offsetting the initial complexity of implementing multiple observation points.
Data Source
AI summary
An object of the present disclosure is to provide a method capable of estimating the radio quality more accurately. The method includes acquiring first weather information and first geographic information at a first observation point, second weather information and second geographic information at a second observation point, and radio quality information at a radio base station; learning a model for estimating radio quality based on the first weather information and the first geographic information, the second weather information and the second geographic information, and the radio quality information; and estimating the radio quality using the model and at least one of third weather information at the first observation point or fourth weather information at the second observation point.