ML-Based Satellite Beam Assignment for Efficiency
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Solution Overview
Problem
Satellite communication systems face inefficiencies due to suboptimal spot beam assignments for terminals, leading to weak signals and low modulation and coding efficiency, especially for those located near the edge of beam coverage areas.
Innovation Solution
The implementation of machine learning models that analyze data from satellite terminals to predict and adjust spot beam assignments based on geographic location and efficiency measures, identifying terminals that can benefit from beam changes to improve communication efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If terminals are assigned to spot beams based on initial installation configuration, then device complexity is reduced and ease of operation is improved, but communication efficiency deteriorates for terminals near beam coverage edges
Solution Approach 1:
The patent implements dynamic beam assignment that allows terminals to switch between spot beams based on real-time communication conditions. The system monitors communication efficiency metrics and automatically reassigns terminals to optimal beams, transforming the static initial assignment into a dynamic, adaptive configuration that maintains high efficiency while requiring minimal user intervention.
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring communication efficiency metrics for each terminal and using this information to determine optimal beam assignments. The feedback loop enables the system to identify terminals experiencing poor performance and automatically reassign them to more suitable beams, thereby resolving the contradiction between initial simplicity and ongoing efficiency.
2Productivity
If machine learning models are used to predict and adjust spot beam assignments, then communication efficiency is improved, but device complexity and system complexity increase
Solution Approach 1:
The patent introduces machine learning models as intermediary components that process terminal data and beam assignment criteria to predict optimal assignments. These models act as intelligent mediators between raw communication data and beam assignment decisions, enabling efficient optimization without requiring complex manual configuration or real-time manual intervention, thus managing complexity while improving efficiency.
Solution Approach 2:
The system performs preliminary actions by using machine learning models to pre-calculate and predict optimal beam assignments based on historical data and terminal characteristics. This preliminary analysis enables the system to proactively assign terminals to optimal beams before communication issues arise, reducing the need for reactive adjustments and simplifying ongoing system management.
3Area of stationary object
If terminals near beam coverage edges use overlapping spot beams, then coverage area is expanded, but signal strength and communication efficiency deteriorate
Solution Approach 1:
The patent applies local quality by assigning different spot beams to different terminal locations based on their specific geographic and communication characteristics. Terminals in optimal positions receive assignments to beams providing strong signals, while terminals near coverage edges are dynamically reassigned to alternative beams that provide better signal strength, ensuring that each local area receives the most appropriate beam assignment for its specific conditions.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for machine learning models for adjusting communication parameters. In some implementations, data for each device in a set of multiple communication devices is obtained. A machine learning model is trained based on the obtained data. The model can be trained to receive an indication of a geographic location and predict a communication setting capable of providing at least a minimum level of efficiency. After training the machine learning model, an indication of a predicted communication setting for a particular communication device is generated. A determination is then made whether to change a current communication setting for the particular communication device based on the predicted communication setting.