Autonomous Satellite Relay Path Optimization via Reinforcement Learning
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Solution Overview
Problem
Conventional systems for satellite level communications require significant human intervention and fail to dynamically leverage network capacity, resulting in suboptimal data transmission rates and coverage due to the lack of autonomous management and path optimization.
Innovation Solution
A computing system utilizing Artificial Neural Networks (ANNs) to determine optimal communication paths and configurations for satellite information transmission, autonomously selecting the best routes and configurations based on real-time states and input parameters, thereby enhancing data transmission efficiency and resilience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional systems use human operators with heuristic algorithms to manage satellite communications, then system complexity is reduced and ease of operation is improved, but automation level is low and data transmission efficiency deteriorates
Solution Approach 1:
The system employs autonomous agents deployed on satellites that automatically perform communication management tasks including path selection, relay coordination, and data transmission optimization without requiring human intervention. The agents independently evaluate network states and execute decisions based on learned policies from reinforcement learning training.
Solution Approach 2:
The patent replaces manual human operation with automated software agents that use machine learning algorithms to manage satellite communications. The reinforcement learning models substitute for human decision-making processes in selecting communication paths and coordinating relays.
2Productivity
If conventional systems use static routing methods to transmit satellite data, then device complexity is reduced, but data transmission speed and network capacity utilization deteriorate
Solution Approach 1:
The system implements dynamic routing where communication paths are continuously adjusted based on real-time network conditions, satellite positions, and traffic demands. The autonomous agents evaluate multiple potential paths and select optimal routes dynamically rather than using predetermined static routes.
Solution Approach 2:
The reinforcement learning agents receive feedback about network state, transmission success, and performance metrics to continuously optimize routing decisions. The system learns from past communications and adapts its path selection strategy based on observed outcomes and changing conditions.
3Reliability
If conventional systems lack autonomous path optimization, then system complexity is reduced, but communication resiliency and network capacity leverage deteriorate
Solution Approach 1:
The system pre-trains reinforcement learning agents through extensive simulation before deployment, enabling them to make intelligent routing decisions from the start. The agents are pre-equipped with knowledge of optimal communication strategies learned during the training phase.
Solution Approach 2:
The patent introduces autonomous software agents as intermediaries between satellites to coordinate communications and select optimal paths. These agents act as mediators that manage the complexity of multi-satellite coordination without requiring direct human intervention or complex satellite hardware.
Data Source
AI summary
A system and method for facilitating autonomous satellite level communications is disclosed. The method includes receiving a request from a user to transmit satellite information captured by a source satellite from the source satellite to a destination node via one or more intermediate satellites. The method further includes receiving one or more input parameters and determining a plurality of states corresponding to the source satellite and the one or more intermediate satellites. Further, the method includes generating a plurality of actions and an expected reward value for each of the plurality of actions. Furthermore, the method includes determining an optimal action among the plurality of actions and outputting the optimal action to the source satellite and the one or more intermediate satellites, one or more user devices or a combination thereof for transmission of the satellite information from the source satellite to the destination node.