1.6k citations · 6.8k across the 149 of their papers we have counts for
151 papers
Simultaneous Beam Training and Target Sensing in ISAC Systems with RIS
Kangjian Chen, Chenhao Qi, Octavia A. Dobre +1
This paper investigates an integrated sensing and communication (ISAC) system with reconfigurable intelligent surface (RIS). Our simultaneous beam training and target sensing (SBTT…
RIS-Enhanced Semantic Communications Adaptive to User Requirements
Peiwen Jiang, Chao-Kai Wen, Shi Jin +1
Semantic communication significantly reduces required bandwidth by understanding semantic meaning of the transmitted. However, current deep learning-based semantic communication me…
Sparse Decentralized Federated Learning
Shan Sha, Shenglong Zhou, Lingchen Kong +1
Decentralized Federated Learning (DFL) enables collaborative model training without a central server but faces challenges in efficiency, stability, and trustworthiness due to commu…
Deep Unfolding-Based Channel Estimation for Wideband TeraHertz Near-Field Massive MIMO Systems
Jiabao Gao, Xiaoming Cheng, Geoffrey Ye Li
The combination of Terahertz (THz) and massive multiple-input multiple-output (MIMO) is promising to meet the increasing data rate demand of future wireless communication systems t…
Deep Plug-and-Play Prior for Multitask Channel Reconstruction in Massive MIMO Systems
Weixiao Wan, Wei Chen, Shiyue Wang +2
Scalability is a major concern in implementing deep learning (DL) based methods in wireless communication systems. Given various channel reconstruction tasks, applying one DL model…
Semantic-Aware Image Compressed Sensing
Bowen Zhang, Zhijin Qin, Geoffrey Ye Li
Deep learning based image compressed sensing (CS) has achieved great success. However, existing CS systems mainly adopt a fixed measurement matrix to images, ignoring the fact the…
Deep-Unfolding for Next-Generation Transceivers
Qiyu Hu, Yunlong Cai, Guangyi Zhang +2
The stringent performance requirements of future wireless networks, such as ultra-high data rates, extremely high reliability and low latency, are spurring worldwide studies on def…
QoE-based Semantic-Aware Resource Allocation for Multi-Task Networks
Lei Yan, Zhijin Qin, Chunfeng Li +3
By transmitting task-related information only, semantic communications yield significant performance gains over conventional communications. However, the lack of mature semantic th…
Semantic Communication with Memory
Huiqiang Xie, Zhijin Qin, Geoffrey Ye Li
While semantic communication succeeds in efficiently transmitting due to the strong capability to extract the essential semantic information, it is still far from the intelligent o…
Semantic Communications with Variable-Length Coding for Extended Reality
Bowen Zhang, Zhijin Qin, Geoffrey Ye Li
Wireless extended reality (XR) has attracted wide attentions as a promising technology to improve users' mobility and quality of experience. However, the ultra-high data rate requi…
AMP-SBL Unfolding for Wideband MmWave Massive MIMO Channel Estimation
Jiabao Gao, Caijun Zhong, Geoffrey Ye Li
In wideband millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, channel estimation is challenging due to the hybrid analog-digital architecture, which c…
Semantic Sensing and Communications for Ultimate Extended Reality
Bowen Zhang, Zhijin Qin, Yiyu Guo +1
As a key technology in metaversa, wireless ultimate extended reality (XR) has attracted extensive attentions from both industry and academia. However, the stringent latency and ult…
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet
Kaidi Xu, Nguyen Van Huynh, Geoffrey Ye Li
In heterogeneous networks (HetNets), the overlap of small cells and the macro cell causes severe cross-tier interference. Although there exist some approaches to address this probl…
Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions
Mengyuan Lee, Guanding Yu, Huaiyu Dai +1
As an efficient graph analytical tool, graph neural networks (GNNs) have special properties that are particularly fit for the characteristics and requirements of wireless communica…
Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO
Houssem Sifaou, Geoffrey Ye Li
Cell-free massive MIMO is emerging as a promising technology for future wireless communication systems, which is expected to offer uniform coverage and high spectral efficiency com…
CSI-PPPNet: A One-Sided One-for-All Deep Learning Framework for Massive MIMO CSI Feedback
Wei Chen, Weixiao Wan, Shiyue Wang +3
To reduce multiuser interference and maximize the spectrum efficiency in orthogonal frequency division duplexing massive multiple-input multiple-output (MIMO) systems, the downlink…
Wireless Semantic Transmission via Revising Modules in Conventional Communications
Peiwen Jiang, Chao-Kai Wen, Shi Jin +1
Semantic communication has become a popular research area due its high spectrum efficiency and error-correction performance. Some studies use deep learning to extract semantic feat…
0/1 Constrained Optimization Solving Sample Average Approximation for Chance Constrained Programming
Shenglong Zhou, Lili Pan, Naihua Xiu +1
Sample average approximation (SAA) is a tractable approach for dealing with chance constrained programming, a challenging stochastic optimization problem. The constraint of SAA is…
Learn to Adapt to New Environment from Past Experience and Few Pilot
Ouya Wang, Jiabao Gao, Geoffrey Ye Li
In recent years, deep learning has been widely applied in communications and achieved remarkable performance improvement. Most of the existing works are based on data-driven deep l…
LEO Satellite-Enabled Grant-Free Random Access with MIMO-OTFS
Boxiao Shen, Yongpeng Wu, Wenjun Zhang +3
This paper investigates joint channel estimation and device activity detection in the LEO satellite-enabled grant-free random access systems with large differential delay and Doppl…
Exact Penalty Method for Federated Learning
Shenglong Zhou, and Geoffrey Ye Li
Federated learning has burgeoned recently in machine learning, giving rise to a variety of research topics. Popular optimization algorithms are based on the frameworks of the (stoc…
Hybrid Precoding for Mixture Use of Phase Shifters and Switches in mmWave Massive MIMO
Chenhao Qi, Qiang Liu, Xianghao Yu +1
A variable-phase-shifter (VPS) architecture with hybrid precoding for mixture use of phase shifters and switches, is proposed for millimeter wave massive multiple-input multiple-ou…
Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems
Jiajia Guo, Chao-Kai Wen, Shi Jin +1
Many performance gains achieved by massive multiple-input and multiple-output depend on the accuracy of the downlink channel state information (CSI) at the transmitter (base statio…
0/1 Deep Neural Networks via Block Coordinate Descent
Hui Zhang, Shenglong Zhou, Geoffrey Ye Li +1
The step function is one of the simplest and most natural activation functions for deep neural networks (DNNs). As it counts 1 for positive variables and 0 for others, its intrinsi…