13 citations · 32 across the 11 of their papers we have counts for
12 papers
One-Step Generative Channel Estimation via Average Velocity Field
Zehua Jiang, Fenghao Zhu, Siming Jiang +5
Generative models have shown immense potential for wireless communication by learning complex channel data distributions. However, the iterative denoising process associated with t…
Recursive Flow: A Generative Framework for MIMO Channel Estimation
Zehua Jiang, Fenghao Zhu, Chongwen Huang +5
Channel estimation is a fundamental challenge in massive multiple-input multiple-output systems, where estimation accuracy governs the spectral efficiency and link reliability. In…
DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications
Bohao Wang, Zehua Jiang, Zhenyu Yang +9
Domain-specific datasets are the foundation for unleashing artificial intelligence (AI)-driven wireless innovation. Yet existing wireless AI corpora are slow to produce, offer limi…
Multi-Sources Fusion Learning for Multi-Points NLOS Localization in OFDM System
Bohao Wang, Zitao Shuai, Chongwen Huang +7
Accurate localization of mobile terminals is a pivotal aspect of integrated sensing and communication systems. Traditional fingerprint-based localization methods, which infer coord…
Robust Continuous-Time Beam Tracking with Liquid Neural Network
Fenghao Zhu, Xinquan Wang, Chongwen Huang +6
Millimeter-wave (mmWave) technology is increasingly recognized as a pivotal technology of the sixth-generation communication networks due to the large amounts of available spectrum…
An Edge-Cloud Collaboration Framework for Generative AI Service Provision with Synergetic Big Cloud Model and Small Edge Models
Yuqing Tian, Zhaoyang Zhang, Yuzhi Yang +5
Generative artificial intelligence (GenAI) offers various services to users through content creation, which is believed to be one of the most important components in future network…
Joint Compression and Deadline Optimization for Wireless Federated Learning
Maojun Zhang, Yang Li, Dongzhu Liu +4
Federated edge learning (FEEL) is a popular distributed learning framework for privacy-preserving at the edge, in which densely distributed edge devices periodically exchange model…
Mobile MIMO Channel Prediction with ODE-RNN: a Physics-Inspired Adaptive Approach
Zhuoran Xiao, Zhaoyang Zhang, Zirui Chen +2
Obtaining accurate channel state information (CSI) is crucial and challenging for multiple-input multiple-output (MIMO) wireless communication systems. Conventional channel estimat…
Gradient Obfuscation Gives a False Sense of Security in Federated Learning
Kai Yue, Richeng Jin, Chau-Wai Wong +2
Federated learning has been proposed as a privacy-preserving machine learning framework that enables multiple clients to collaborate without sharing raw data. However, client priva…
Neural Tangent Kernel Empowered Federated Learning
Kai Yue, Richeng Jin, Ryan Pilgrim +3
Federated learning (FL) is a privacy-preserving paradigm where multiple participants jointly solve a machine learning problem without sharing raw data. Unlike traditional distribut…
Federated Learning via Plurality Vote
Kai Yue, Richeng Jin, Chau-Wai Wong +1
Federated learning allows collaborative workers to solve a machine learning problem while preserving data privacy. Recent studies have tackled various challenges in federated learn…
Communication Efficient Federated Learning with Energy Awareness over Wireless Networks
Richeng Jin, Xiaofan He, Huaiyu Dai
In federated learning (FL), reducing the communication overhead is one of the most critical challenges since the parameter server and the mobile devices share the training paramete…