7 papers
Towards a Joint Task-Oriented and Generative Semantic Communication Framework for 6G Networks
Soheyb Ribouh, Phil Polo Ditsia Di Ngoma
Semantic Communication (SC) has emerged as a key enabler for 6G wireless systems by transmitting task-relevant meaning rather than raw data, thereby significantly reducing bandwidt…
Graph Based Semantic Encoder Decoder Framework for Task Oriented Communications in Connected Autonomous Vehicles
Soheyb Ribouh, Phil Polo Ditsia Di Ngoma
Connected autonomous vehicles (CAVs) require reliable and efficient communication frameworks to support safety critical and task-oriented applications such as collision avoidance,…
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…
Differential Transformer-driven 6G Physical Layer for Collaborative Perception Enhancement
Soheyb Ribouh, Osama Saleem, Mohamed Ababsa
The emergence of 6G wireless networks promises to revolutionize vehicular communications by enabling ultra-reliable, low-latency, and high-capacity data exchange. In this context,…
Deep Multimodal Learning for Real-Time DDoS Attacks Detection in Internet of Vehicles
Mohamed Ababsa, Soheyb Ribouh, Abdelhamid Malki +1
The progress and integration of intelligent transport systems (ITS) have therefore been central to creating safer and more efficient transport networks. The Internet of Vehicles (I…
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…