612 citations · 2.7k across the 63 of their papers we have counts for
7 papers · 1 filter
Efficient STAR-RIS Mode for Energy Minimization in WPT-FL Networks with NOMA
MohammadHossien Alishahi, Ming Zeng, Paul Fortier +5
With the massive deployment of IoT devices in 6G networks, several critical challenges have emerged, such as large communication overhead, coverage limitations, and limited battery…
A Coopetitive-Compatible Data Generation Framework for Cross-silo Federated Learning
Thanh Linh Nguyen, Quoc-Viet Pham
Cross-silo federated learning (CFL) enables organizations (e.g., hospitals or banks) to collaboratively train artificial intelligence (AI) models while preserving data privacy by k…
Diffusion Models for Future Networks and Communications: A Comprehensive Survey
Nguyen Cong Luong, Nguyen Duc Hai, Duc Van Le +9
The rise of Generative AI (GenAI) in recent years has catalyzed transformative advances in wireless communications and networks. Among the members of the GenAI family, Diffusion Mo…
Integration of TinyML and LargeML: A Survey of 6G and Beyond
Thai-Hoc Vu, Ngo Hoang Tu, Thien Huynh-The +4
The evolution from fifth-generation (5G) to sixth-generation (6G) networks is driving an unprecedented demand for advanced machine learning (ML) solutions. Deep learning has alread…
Recent Advances in Near-Field Beam Training and Channel Estimation for XL-MIMO Systems
Ming Zeng, Ji Wang, Wanming Hao +3
Extremely large-scale multiple-input multiple-output (XL-MIMO) is a key technology for next-generation wireless communication systems. By deploying significantly more antennas than…
"Security for Everyone" in Finite Blocklength IRS-aided Systems With Perfect and Imperfect CSI
Monir Abughalwa, Diep N. Nguyen, Dinh Thai Hoang +4
Provisioning secrecy for all users, given the heterogeneity in their channel conditions, locations, and the unknown location of the attacker/eavesdropper, is challenging and not al…
Carpe Diem: Critical Learning Period-Aware Contract-Based Incentives for Federated Learning
Thanh Linh Nguyen, Dinh Thai Hoang, Diep N. Nguyen +1
Critical learning periods (CLPs) in federated learning (FL) refer to early stages during which low-quality contributions (e.g., sparse training data availability) can permanently i…