5 papers
Beyond Dark Knowledge: Mixup-Based Distillation for Reliable Predictions
José Medina, Paul Honeine, Abdelaziz Bensrhair +1
Knowledge Distillation (KD) and mixup have proven effective at inducing smoothness in class boundaries; KD captures inherent class relationships in probability distributions, and m…
Learning-Based Hybrid Neural Receiver for 6G-V2X Communications
Osama Saleem, Mohammed Alfaqawi, Pierre Merdrignac +2
Neural receiver models are proposed to jointly optimize multiple functionalities of wireless receivers; however, a comprehensive receiver model that replaces the entire physical la…
Mamba base PKD for efficient knowledge compression
José Medina, Amnir Hadachi, Paul Honeine +1
Deep neural networks (DNNs) have remarkably succeeded in various image processing tasks. However, their large size and computational complexity present significant challenges for d…
Deep Multi-modal Neural Receiver for 6G Vehicular Communication
Osama Saleem, Mohammed Alfaqawi, Pierre Merdrignac +2
Deep Learning (DL) based neural receiver models are used to jointly optimize PHY of baseline receiver for cellular vehicle to everything (C-V2X) system in next generation (6G) comm…
TransRx-6G-V2X : Transformer Encoder-Based Deep Neural Receiver For Next Generation of Cellular Vehicular Communications
Osama Saleem, Soheyb Ribouh, Mohammed Alfaqawi +2
End-to-end wireless communication is new concept expected to be widely used in the physical layer of future wireless communication systems (6G). It involves the substitution of tra…