Orbital Optical Communication Rerouting via AI
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Solution Overview
Problem
Satellite-to-ground communication using optical signals is hindered by cloudy weather and atmospheric conditions, which disrupt the transmission of information between orbital and terrestrial communication nodes in existing systems.
Innovation Solution
An automated and cognitive-based computing system employing artificial intelligence and machine learning algorithms dynamically manages optical communication signals by sensing, predicting, and inferring network conditions, allowing for the rerouting of information via a hybrid mesh network topology, ensuring continuous transmission through alternative routes when direct paths are obstructed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If optical communication signals are used for satellite-to-ground communication, then data transfer efficiency and bandwidth are improved, but transmission reliability deteriorates due to cloudy weather and atmospheric conditions
Solution Approach 1:
The system dynamically switches between optical communication mode and RF communication mode based on real-time atmospheric conditions. When cloud cover or atmospheric disturbances are detected, the system transitions from optical signals to RF signals to maintain continuous, reliable communication, thus resolving the contradiction between high data transfer efficiency and transmission reliability.
Solution Approach 2:
The system changes the communication parameter (signal type) from optical to RF based on environmental conditions. This parameter change allows the system to adapt to varying atmospheric transparency, maintaining both high productivity when conditions are favorable and high reliability when conditions deteriorate.
2Speed
If a direct optical transmission path is used between orbital node and target terrestrial node, then communication speed is improved, but transmission stability deteriorates when atmospheric conditions obstruct the path
Solution Approach 1:
The system introduces an intermediary communication path through alternative terrestrial nodes. When the direct optical path is blocked by atmospheric conditions, data is transmitted through intermediate nodes using RF signals or alternative optical paths, maintaining transmission stability while preserving communication speed through efficient routing.
Solution Approach 2:
The system dynamically selects transmission paths based on real-time atmospheric conditions. When the direct path is clear, high-speed optical transmission is used. When obstruction is detected, the system dynamically switches to alternative paths through intermediate nodes, maintaining both speed and stability adaptively.
3Reliability
If RF signals are used for satellite-to-ground communication, then transmission reliability through atmospheric conditions is improved, but data transfer bandwidth deteriorates
Solution Approach 1:
The system periodically monitors atmospheric conditions and switches between RF and optical modes accordingly. During clear periods, optical communication provides high bandwidth. During adverse periods, RF communication ensures reliable transmission. This periodic switching based on environmental conditions resolves the contradiction between reliability and bandwidth.
Solution Approach 2:
The communication system is designed with multi-functionality, supporting both RF and optical communication modes. This universal design allows the system to leverage the high bandwidth of optical signals when available and the high reliability of RF signals when needed, achieving both objectives across different operating conditions.
Data Source
AI summary
The present disclosure describes an automated, cognitive based computing system using artificial intelligence (AI) and machine learning algorithms to sense, predict, and infer network conditions, configured to dynamically manage transmission of information between communication nodes. The communication nodes comprise orbital nodes positioned in orbit above earth and terrestrial nodes coupled with earth interconnected via a hybrid mesh network topology. One or more automated, cognitive based physical computing processors, using artificial intelligence (AI) and machine learning algorithms to sense, predict, and infer network conditions, determine a target terrestrial node to receive information initially stored on a first orbital node; determine transmission conditions between the target terrestrial node and the first orbital node based on output signals from sensors; dynamically determine whether transmission conditions between the first orbital node and the target terrestrial node prevent optical transmission of the information directly from the first orbital node to the target terrestrial node; and, responsive to a determination that transmission conditions prevent optical transmission of the information to the target terrestrial node from the first orbital node, automatically transmit the information along an alternate route between the first orbital node and the target terrestrial node, wherein the alternate route includes transmission between some orbital node and an alternative target terrestrial node other than the target terrestrial node.