Zobrazeno 1 - 10
of 1 565
pro vyhledávání: '"LETAÏEF, A."'
Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) are being explored for the next generation of sixth-generation (6G) networks. A promising configuration for their deployment is within cell-free massive multiple
Externí odkaz:
http://arxiv.org/abs/2411.14030
To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication.
Externí odkaz:
http://arxiv.org/abs/2411.13104
Fine-tuning large pre-trained foundation models (FMs) on distributed edge devices presents considerable computational and privacy challenges. Federated fine-tuning (FedFT) mitigates some privacy issues by facilitating collaborative model training wit
Externí odkaz:
http://arxiv.org/abs/2411.07806
This paper presents a semantic-aware multi-modal resource allocation (SAMRA) for multi-task using multi-agent reinforcement learning (MARL), termed SAMRAMARL, utilizing in platoon systems where cellular vehicle-to-everything (C-V2X) communication is
Externí odkaz:
http://arxiv.org/abs/2411.04672
Autor:
Liu, Zhang, Du, Hongyang, Hou, Xiangwang, Huang, Lianfen, Hosseinalipour, Seyyedali, Niyato, Dusit, Letaief, Khaled Ben
Generative AI (GenAI) has emerged as a transformative technology, enabling customized and personalized AI-generated content (AIGC) services. In this paper, we address challenges of edge-enabled AIGC service provisioning, which remain underexplored in
Externí odkaz:
http://arxiv.org/abs/2411.01458
This paper investigates distributed computing and cooperative control of connected and automated vehicles (CAVs) in ramp merging scenario under transportation cyber-physical system. Firstly, a centralized cooperative trajectory planning problem is fo
Externí odkaz:
http://arxiv.org/abs/2410.22987
The Internet of Vehicles (IoV) network can address the issue of limited computing resources and data processing capabilities of individual vehicles, but it also brings the risk of privacy leakage to vehicle users. Applying blockchain technology can e
Externí odkaz:
http://arxiv.org/abs/2409.17287
Autor:
Wang, Ouya, He, Hengtao, Zhou, Shenglong, Ding, Zhi, Jin, Shi, Letaief, Khaled B., Li, Geoffrey Ye
The integration with artificial intelligence (AI) is recognized as one of the six usage scenarios in next-generation wireless communications. However, several critical challenges hinder the widespread application of deep learning (DL) techniques in w
Externí odkaz:
http://arxiv.org/abs/2409.04302
Intelligent Transportation Systems (ITS) leverage Integrated Sensing and Communications (ISAC) to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles (IoV). This integration inevitably increases computing demands, ri
Externí odkaz:
http://arxiv.org/abs/2408.14831
In the Internet of Vehicles (IoV), Federated Learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data. Traditional supervised learning requires image data with labels, but data labeling involves significan
Externí odkaz:
http://arxiv.org/abs/2408.09194