Satellite Carrier Acquisition Using Machine Learning Spectrum Analysis
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Solution Overview
Problem
Existing satellite communication systems face challenges in efficiently identifying and selecting optimal satellite communication carriers due to overlapping satellite beam coverage and dynamic changes in carrier availability.
Innovation Solution
A satellite terminal equipped with a processor and memory that executes instructions to input a frequency spectrum distribution to a machine learning program, such as a neural network, to identify and select the best satellite communication carriers based on trained metadata, updating the carrier list dynamically and ensuring successful locking to the selected carriers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If satellite terminals manually identify and select carriers from overlapping satellite beams, then carrier selection can be made, but the process becomes complex and time-consuming due to dynamic changes in carrier availability and beam coverage
Solution Approach 1:
The machine learning model enables the satellite terminal to automatically identify and select optimal carriers without manual intervention. The system self-services by processing frequency spectrum distributions and making carrier selection decisions autonomously, eliminating the need for complex manual carrier selection procedures while adapting to dynamic changes in carrier availability and beam coverage
Solution Approach 2:
The patent replaces manual carrier identification and selection processes with an automated machine learning-based system. The mechanical/manual process of analyzing frequency spectrum distributions and selecting carriers is substituted with an electronic/algorithmic system that automatically processes spectral data and makes intelligent carrier selection decisions based on trained models
2Measurement precision
If traditional methods are used to identify satellite communication carriers in frequency spectrum distributions, then carrier identification can be performed, but the accuracy and efficiency are reduced due to overlapping beam coverage and dynamic carrier availability
Solution Approach 1:
The machine learning model is trained in advance using labeled frequency spectrum distribution data to learn the characteristics of different carriers and their patterns in overlapping beam environments. This preliminary training action enables the model to quickly and accurately identify carriers during operation without time-consuming manual analysis, as the identification logic has been pre-established through training on diverse spectral scenarios
Solution Approach 2:
The system uses trained machine learning models that capture the essential patterns and characteristics of carrier identification from extensive training data. Instead of performing complex real-time analysis from scratch, the system copies the learned identification patterns from the training phase and applies them to new frequency spectrum distributions, enabling fast and accurate carrier identification that replicates expert-level performance
Data Source
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AI summary
A satellite terminal comprises a processor and a memory. The memory stores instructions executable by the processor to determine a frequency spectrum distribution for a wireless communication signal received from an antenna, to input the determined frequency spectrum distribution of the received wireless communication signal to a machine learning program trained to identify one or more satellite communication carriers in a frequency spectrum distribution, to receive a list of one or more satellite communication carriers from the trained machine learning program, and to lock the satellite terminal to one of the carriers included in the list of one or more satellite communication carriers.