Deep learning at the physical layer: System challenges and applications to 5G and beyond
F Restuccia, T Melodia - IEEE Communications Magazine, 2020 - ieeexplore.ieee.org
The unprecedented requirements of IoT have made fine-grained optimization of spectrum
resources an urgent necessity. Thus, designing techniques able to extract knowledge from …
resources an urgent necessity. Thus, designing techniques able to extract knowledge from …
Deep Learning at the Physical Layer: System Challenges and Applications to 5G and Beyond
F Restuccia, T Melodia - arXiv e-prints, 2020 - ui.adsabs.harvard.edu
The unprecedented requirements of the Internet of Things (IoT) have made fine-grained
optimization of spectrum resources an urgent necessity. Thus, designing techniques able to …
optimization of spectrum resources an urgent necessity. Thus, designing techniques able to …
Deep Learning at the Physical Layer: System Challenges and Applications to 5G and Beyond
F Restuccia, T Melodia - arXiv preprint arXiv:2004.10113, 2020 - arxiv.org
The unprecedented requirements of the Internet of Things (IoT) have made fine-grained
optimization of spectrum resources an urgent necessity. Thus, designing techniques able to …
optimization of spectrum resources an urgent necessity. Thus, designing techniques able to …
[PDF][PDF] Deep Learning at the Physical Layer: System Challenges and Applications to 5G and Beyond
F Restuccia, T Melodia - IEEE Communications Magazine, 2020 - ece.northeastern.edu
The unprecedented requirements of IoT have made fine-grained optimization of spectrum
resources an urgent necessity. Thus, designing techniques able to extract knowledge from …
resources an urgent necessity. Thus, designing techniques able to extract knowledge from …