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Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systems
Simulation results over different urban vehicular mobility scenarios show that the proposed T2U method provides a probability of correct association that increases with the size of the BS antenna array, highlighting the respective increase of the separability of the VEs in the beamspace.
A Multi-Modal Simulation Framework to Enable Digital Twin-based V2X Communications in Dynamic Environments
Digital Twins (DTs) for physical wireless environments have been recently proposed as accurate virtual representations of the propagation environment that can enable multi-layer decisions at the physical communication equipment.
SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning
In this context, federated learning is one of the most influential frameworks for privacy-preserving distributed machine learning, achieving astounding results in many natural language processing and computer vision tasks.
Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications
Algebraic Low-rank (LR) channel estimation exploits space-time channel sparsity through the computation of position-dependent MIMO channel eigenmodes leveraging recurrent training vehicle passages in the coverage cell.
