Deep Learning Rain Fade Forecasting for Satellite Links
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Solution Overview
Problem
Existing rain fade prediction models, particularly empirical and statistical methods, struggle to provide accurate long-term forecasts and are not scalable to large systems, making proactive resource management difficult.
Innovation Solution
A deep learning-based system utilizing satellite and radar imagery data, along with link power measurements, to forecast rain fade events up to 65 minutes into the future, employing a neural network trained with pre-processed data to enhance accuracy and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If empirical and statistical models are used for rain fade prediction, then the models can predict near-future rain fade events, but they cannot forecast far into the future and are not scalable to large-scale systems
Solution Approach 1:
The patent replaces traditional empirical and statistical mechanical models with a deep learning-based neural network system. The neural network processes multi-source data (satellite imagery, radar data, beacon measurements) through learned patterns rather than predefined statistical relationships, enabling both high accuracy and extended forecast horizons up to 65 minutes into the future.
Solution Approach 2:
The system transforms the prediction approach by changing from static statistical parameters to dynamic multi-dimensional parameters including satellite image data, radar reflectivity, beacon signal strength, and temporal sequences. This parameter transformation enables the model to capture complex atmospheric patterns that evolve over extended time horizons.
2Device complexity
If empirical and statistical models are used for rain fade prediction, then the models can operate with limited data, but they cannot be generalized to large-scale systems
Solution Approach 1:
The deep learning model serves multiple functions simultaneously: it processes diverse data types (images, radar, beacon data), performs both short-term and long-term predictions, and can be deployed across multiple geolocations. The single neural network architecture handles various forecast horizons and different spatial regions, making the system universally applicable to large-scale satellite communication networks.
Solution Approach 2:
The system adds temporal and spatial dimensions to the prediction model by incorporating time-series beacon data, sequential satellite imagery, and radar reflectivity over time. This multi-dimensional approach enables the model to generalize across different locations and time periods, transforming a simple prediction tool into a scalable system capable of handling large-scale deployments.
3Use of energy by moving object
If traditional machine learning algorithms are used for rain fade forecasting, then the computational requirements are lower, but the forecasting accuracy in both near and long term is inferior
Solution Approach 1:
The system performs preliminary data processing and feature extraction during the training phase, pre-learning patterns from extensive historical data. This preliminary action enables the model to make accurate predictions with lower computational requirements during real-time operation, as the heavy lifting of pattern recognition has already been performed during training.
Data Source
AI summary
A system and method for predicting rain fade. The method may include: training a Neural Network (NN) by importing into the NN a training set of image information and beacon information, wherein the image information includes image datasets including of a cloud view of an Area of Interest (AoI), a geolocation and a timestamp, and the beacon information includes beacon datasets including a beacon strength, a current rain fade state, a geolocation and a timestamp; pre-processing to homogenize and to extract spatially and temporally matching data for the AoI from a live image information and a live beacon information; and forecasting a rain fade based on the data in a near-future. The geolocation of the beacon datasets is located within the AoI, and the periodicity of the live beacon information and the live image information is less than or equal to five (5) minutes.