Direct radiating antennas: Satellites
Reinforcement Learning: Embedded Software
Context
Satellite companies are unable to accurately control their antennas using traditional methodologies.
Problem
Maximize the beam efficiently and minimize the interference with other antennas beams
Objective
Create a small embedded model for real time control of the antenna beam
Challenge
Supervised learning is not working in this context
Solution
By utilizing a NeurEco reinforcement learning algorithm, the antennas produced results outperforming all other previously used methods.
Results
NeurEco’s produced model beat all of their previous model allowing them to improved their overall product.
Benefits to the client
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Increased product reliability resulting in improved customer retention and profitability
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More time to focus on other product development
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Client was so impressed by the improvement they decided to embed the algorithm in their antennas. This was not the original aim of the project