HPC Satellite Modem Machine Learning Dynamic Beam Switching
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Solution Overview
Problem
Current satellite communication systems lack flexibility and adaptability to handle rapidly changing environments and mobile satellite constellations, relying on purpose-built hardware that cannot support machine learning or reconfiguration to optimize communications in dynamic conditions.
Innovation Solution
A communication network with a high-performance computer (HPC)-based satellite modem configured with machine learning capability, integrated with access to repeating and regenerative relays, and directional antennas, enabling dynamic evaluation and selection of satellites based on heterogeneous data sources for proactive network optimization and disruption avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If purpose-built proprietary hardware is used for fixed satellite operation, then system reliability is improved, but adaptability to rapidly changing environments deteriorates
Solution Approach 1:
The patent implements dynamic beam switching and satellite selection capabilities that allow the system to adapt to changing environmental conditions, mobile satellite constellations, and adverse weather events in real-time, transforming the previously static fixed-satellite system into a dynamic one that can respond to changing conditions while maintaining reliable communication
2Productivity
If machine learning capability is integrated into the satellite modem, then network optimization is improved, but device complexity increases
Solution Approach 1:
The patent introduces a machine learning system as an intermediary layer between the satellite modem and the communication network, which processes heterogeneous data sources and makes intelligent decisions about beam switching and satellite selection, thereby optimizing network performance without requiring complex changes to the underlying hardware architecture
3Reliability
If proactive network reconfiguration is implemented, then service quality is improved, but processing time increases
Solution Approach 1:
The patent implements preliminary action by using machine learning to predict adverse conditions and satellite anomalies before they occur, allowing the system to proactively switch beams or satellites in advance, thereby maintaining service quality without experiencing disruption when the predicted events actually occur
Data Source
AI summary
Provided is a communication network comprising a ground station, comprising a modem communicatively coupled to at least one aerial or space communications platform communicatively coupled to at least one communications terminal system, comprising an HPC-based satellite modem configured with machine learning capability for optimization of communications network connections.