Zobrazeno 1 - 10
of 4 943
pro vyhledávání: '"letaief, A."'
Autor:
Yu, Wentao, He, Hengtao, Song, Shenghui, Zhang, Jun, Dai, Linglong, Zheng, Lizhong, Letaief, Khaled B.
In this paper, we explore the potential of artificial intelligence (AI) to address the challenges posed by terahertz ultra-massive multiple-input multiple-output (THz UM-MIMO) systems. We begin by outlining the characteristics of THz UM-MIMO systems,
Externí odkaz:
http://arxiv.org/abs/2412.09839
In response to the practical demands of the ``right to be forgotten" and the removal of undesired data, machine unlearning emerges as an essential technique to remove the learned knowledge of a fraction of data points from trained models. However, ex
Externí odkaz:
http://arxiv.org/abs/2412.01207
Task-oriented communication presents a promising approach to improve the communication efficiency of edge inference systems by optimizing learning-based modules to extract and transmit relevant task information. However, real-time applications face p
Externí odkaz:
http://arxiv.org/abs/2412.00862
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