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Found 7 papers, 0 papers with code
Learning Emergent Random Access Protocol for LEO Satellite Networks
A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) are envisaged to provide a global coverage SAT network in beyond fifth-generation (5G) cellular systems.
Joint Pilot Design and Channel Estimation using Deep Residual Learning for Multi-Cell Massive MIMO under Hardware Impairments
After that, a deep learning based pilot design is proposed to minimize the mean square error (MSE) of LMMSE channel estimation, which is utilized to the joint pilot design and channel estimator for transfer learning approach.
Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication
In this article, we study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user.
Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks
Lastly, thanks to utilizing hybrid FSO/RF links, the proposed scheme achieves up to 62. 56x higher peak throughput and 21. 09x higher worst-case throughput than the cases utilizing either RF or FSO links, highlighting the importance of co-designing SAT-UAV associations, UAV trajectories, and hybrid FSO/RF links in beyond-5G NTNs.
Robust and Low Complexity Beam Tracking with Monopulse Signal for UAV Communications
UAV communications based on an antenna array entail a beam tracking technology for reliable link acquisition.
Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks
A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency.
