1k citations · 2.9k across the 117 of their papers we have counts for
15 papers · 1 filter
Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks
Shimin Gong, Meng Wang, Bo Gu +3
In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories…
Hierarchical Deep Reinforcement Learning for Age-of-Information Minimization in IRS-aided and Wireless-powered Wireless Networks
Shimin Gong, Leiyang Cui, Bo Gu +3
In this paper, we focus on a wireless-powered sensor network coordinated by a multi-antenna access point (AP). Each node can generate sensing information and report the latest info…
Toward BCI-enabled Metaverse: A Joint Learning and Resource Allocation Approach
Nguyen Quang Hieu, Dinh Thai Hoang, Diep N. Nguyen +1
Toward user-driven Metaverse applications with fast wireless connectivity and tremendous computing demand through future 6G infrastructures, we propose a Brain-Computer Interface (…
Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing
Hai M. Nguyen, Nam H. Chu, Diep N. Nguyen +5
Federated Learning (FL) with quantization and deliberately added noise over wireless networks is a promising approach to preserve user differential privacy (DP) while reducing wire…
Label driven Knowledge Distillation for Federated Learning with non-IID Data
Minh-Duong Nguyen, Quoc-Viet Pham, Dinh Thai Hoang +3
In real-world applications, Federated Learning (FL) meets two challenges: (1) scalability, especially when applied to massive IoT networks; and (2) how to be robust against an envi…
When Virtual Reality Meets Rate Splitting Multiple Access: A Joint Communication and Computation Approach
Nguyen Quang Hieu, Diep N. Nguyen, Dinh Thai Hoang +1
Rate Splitting Multiple Access (RSMA) has emerged as an effective interference management scheme for applications that require high data rates. Although RSMA has shown advantages i…