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Found 5 papers, 1 papers with code
Towards Autonomous Satellite Communications: An AI-based Framework to Address System-level Challenges
The next generation of satellite constellations is designed to better address the future needs of our connected society: highly-variable data demand, mobile connectivity, and reaching more under-served regions.
Integrating LSTMs and GNNs for COVID-19 Forecasting
The spread of COVID-19 has coincided with the rise of Graph Neural Networks (GNNs), leading to several studies proposing their use to better forecast the evolution of the pandemic.
Evaluating the progress of Deep Reinforcement Learning in the real world: aligning domain-agnostic and domain-specific research
Deep Reinforcement Learning (DRL) is considered a potential framework to improve many real-world autonomous systems; it has attracted the attention of multiple and diverse fields.
Applicability and Challenges of Deep Reinforcement Learning for Satellite Frequency Plan Design
We analyze different alternatives for each of these elements and characterize their effect.
Deep Reinforcement Learning Architecture for Continuous Power Allocation in High Throughput Satellites
In the coming years, the satellite broadband market will experience significant increases in the service demand, especially for the mobility sector, where demand is burstier.
