3 citations · 6 across the 10 of their papers we have counts for
11 papers
Dual-Layer Over-the-Air Federated Learning in LEO Satellite Networks: Architecture, Key Technologies and Applications
Zhendong Li, Shaojie Wang, Zhou Su +4
Low Earth orbit (LEO) satellite networks are emerging as a pivotal infrastructure for global edge intelligence. In this context, integrating over-the-air (OTA) computation with ada…
RadioMamba: Breaking the Accuracy-Efficiency Trade-off in Radio Map Construction via a Hybrid Mamba-UNet
Honggang Jia, Nan Cheng, Xiucheng Wang +4
Radio map (RM) has recently attracted much attention since it can provide real-time and accurate spatial channel information for 6G services and applications. However, current deep…
On-Demand Multimedia Delivery in 6G: An Optimal-Cost Steiner Tree Approach
Zien Wang, Xiucheng Wang, Nan Cheng +4
The exponential growth of multimedia data traffic in 6G networks poses unprecedented challenges for immersive communication, where ultra-high-definition, multi-quality streaming mu…
RadioDiff-: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction
Xiucheng Wang, Qiming Zhang, Nan Cheng +4
In this paper, we propose a novel physics-informed generative learning approach, named RadioDiff-, for accurate and efficient multipath-aware radio map (RM) construction. As f…
Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G
Ruijin Sun, Nan Cheng, Changle Li +2
In the sixth-generation (6G) networks, newly emerging diversified services of massive users in dynamic network environments are required to be satisfied by multi-dimensional hetero…
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles
Ruijin Sun, Xiao Yang, Nan Cheng +2
By offloading computation-intensive tasks of vehicles to roadside units (RSUs), mobile edge computing (MEC) in the Internet of Vehicles (IoV) can relieve the onboard computation bu…
Knowledge-Driven Resource Allocation for D2D Networks: A WMMSE Unrolled Graph Neural Network Approach
Hao Yang, Nan Cheng, Ruijin Sun +5
This paper proposes an novel knowledge-driven approach for resource allocation in device-to-device (D2D) networks using a graph neural network (GNN) architecture. To meet the milli…
Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach
Xiucheng Wang, Nan Cheng, Lianhao Fu +5
Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. However, the dynamics of edge networks raise two cha…
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning
Xiucheng Wang, Nan Cheng, Longfei Ma +3
In this paper, to deal with the heterogeneity in federated learning (FL) systems, a knowledge distillation (KD) driven training framework for FL is proposed, where each user can se…
SigT: An Efficient End-to-End MIMO-OFDM Receiver Framework Based on Transformer
Ziyou Ren, Nan Cheng, Ruijin Sun +3
Multiple-input multiple-output and orthogonal frequency-division multiplexing (MIMO-OFDM) are the key technologies in 4G and subsequent wireless communication systems. Conventional…
On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks
Longfei Ma, Nan Cheng, Xiucheng Wang +2
On-demand service provisioning is a critical yet challenging issue in 6G wireless communication networks, since emerging services have significantly diverse requirements and the ne…