Multi-Orbit Network Assignment for Passenger Vehicle Connectivity
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Solution Overview
Problem
Existing commercial passenger vehicles face network congestion and connectivity issues due to increased use of personal electronic devices, leading to poor signal quality and degraded video streaming, which negatively impacts passenger experience and productivity.
Innovation Solution
Implementing a network assignment system that utilizes machine learning algorithms to predict and select optimal networks from a hybrid system of satellite networks (GEO and LEO) and cellular networks, dynamically switching to ensure reliable connectivity and efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single network (satellite or cellular) is used for wireless communication services, then the system complexity is low, but network congestion and connectivity issues occur leading to poor signal quality
Solution Approach 1:
The patent combines multiple network types (satellite and cellular networks) into a hybrid network system. The network assignment system evaluates both satellite and cellular networks simultaneously, selecting the optimal network or combination thereof based on real-time conditions, thereby improving signal quality and reliability while managing complexity through automated selection.
Solution Approach 2:
The network assignment system dynamically switches between satellite and cellular networks based on real-time network conditions, vehicle location, and service requirements. This dynamic adaptation allows the system to maintain optimal signal quality by transitioning between networks as conditions change, rather than relying on a static single-network approach.
2Reliability
If multiple networks are used simultaneously, then connectivity reliability improves, but network selection and management complexity increases
Solution Approach 1:
The network assignment system continuously monitors network conditions, vehicle location, and service performance, using this feedback to dynamically adjust network selection. The system evaluates satellite and cellular network parameters in real-time and automatically selects the optimal network, reducing management complexity through automated decision-making based on continuous feedback.
Solution Approach 2:
The network assignment system autonomously performs network selection and switching without requiring manual intervention. It self-manages the complexity of evaluating multiple networks by automatically processing network parameters, making decisions, and executing switches, thereby improving connectivity reliability while keeping the user interface simple.
3Speed
If manual network selection is used, then the system is simple to implement, but response time to network changes is slow affecting passenger experience
Solution Approach 1:
The network assignment system pre-evaluates available networks and prepares switching decisions in advance based on predicted vehicle trajectory and network conditions. By performing preliminary assessments of satellite and cellular network availability and quality, the system can execute rapid switches when conditions change, improving response time without requiring complex real-time decision-making at the moment of switching.
4Productivity
If network resources are allocated statically, then resource allocation is simple, but network congestion occurs during peak demand periods
Solution Approach 1:
The network assignment system dynamically allocates network resources based on real-time demand, vehicle location, and network capacity. It continuously adjusts the selection between satellite and cellular networks to optimize resource utilization, preventing congestion by directing traffic to underutilized networks and improving overall network resource efficiency through adaptive management.
Data Source
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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.