Multi-Orbit Network Assignment for Passenger Connectivity Switching
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Solution Overview
Problem
Existing commercial passenger vehicles face network congestion and connectivity issues due to increased passenger use of personal electronic devices, leading to poor signal quality and degraded video streaming, which negatively impacts passenger experience and productivity.
Innovation Solution
A network assignment system utilizing machine learning algorithms to predict and select optimal networks among satellite and cellular networks, including GEO and LEO satellites, to provide reliable in-flight entertainment services by dynamically switching between network types based on real-time data and historical performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple networks (satellite and cellular) are available for passenger vehicles, then network coverage and service availability are improved, but network selection complexity and system complexity increase
Solution Approach 1:
The system employs machine learning algorithms that automatically analyze network parameters and make autonomous network selection decisions without human intervention. The algorithm processes historical and real-time network data, predicts optimal network characteristics, and automatically switches between satellite and cellular networks based on predicted performance, enabling the system to self-manage network selection complexity
Solution Approach 2:
The system performs preliminary network analysis by collecting and processing historical network parameters before actual network selection is needed. The machine learning algorithm pre-trains on historical data and continuously updates network predictions, so when a triggering event occurs, the optimal network selection is already determined or can be quickly computed based on pre-established patterns
2Measurement precision
If machine learning algorithms are used to predict network characteristics, then network selection accuracy and service quality are improved, but computational complexity and processing time increase
Solution Approach 1:
The system applies machine learning algorithms selectively based on triggering events rather than continuously processing all possible network parameters at full complexity. The algorithm focuses on processing only the most relevant network parameters and historical data needed for the current prediction context, performing partial analysis sufficient for accurate network selection without unnecessary computational overhead
3Reliability
If dynamic network switching is implemented, then connectivity reliability and passenger experience are improved, but network switching frequency and system operational complexity increase
Solution Approach 1:
The system implements continuous feedback loops where network performance is monitored, predictions are made, selections are executed, and results are fed back into the machine learning algorithm for continuous improvement. The algorithm learns from past switching decisions and their outcomes, adjusting future predictions to minimize unnecessary switches while maintaining connectivity reliability, thereby reducing excessive network switching frequency
Data Source
AI summary
A method for managing a network assignment for a commercial passenger vehicle is provided. The method comprises: detecting, at a first time, a triggering event related to wireless communication services provided to passengers in the commercial passenger vehicle; receiving network parameters indicative of historical characteristics of the wireless communication services provided by satellite networks and a cellular network; applying a machine learning algorithm that processes the network parameters and generates output parameters indicative of estimated characteristics of the wireless communication services provided by the satellite networks and the cellular network; and selecting, based on the output parameters, in response to the triggering event, a network for use by the commercial passenger vehicle for providing the wireless communication service at a second time after the first time.