1.6k citations · 6.8k across the 149 of their papers we have counts for
31 papers · 1 filter
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…
Robust Semantic Communications with Masked VQ-VAE Enabled Codebook
Qiyu Hu, Guangyi Zhang, Zhijin Qin +3
Although semantic communications have exhibited satisfactory performance for a large number of tasks, the impact of semantic noise and the robustness of the systems have not been w…
Deep Learning-based Channel Estimation for Wideband Hybrid MmWave Massive MIMO
Jiabao Gao, Caijun Zhong, Geoffrey Ye Li +2
Hybrid analog-digital (HAD) architecture is widely adopted in practical millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems to reduce hardware cost and e…
Over-The-Air Federated Learning under Byzantine Attacks
Houssem Sifaou, Geoffrey Ye Li
Federated learning (FL) is a promising solution to enable many AI applications, where sensitive datasets from distributed clients are needed for collaboratively training a global m…
6G Network AI Architecture for Everyone-Centric Customized Services
Yang Yang, Mulei Ma, Hequan Wu +51
Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to…
CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network
Bowen Zhang, Houssem Sifaou, Geoffrey Ye Li
Deep learning has been widely adopted for channel state information (CSI)-fingerprinting indoor localization systems. These systems usually consist of two main parts, i.e., a posit…
Deep Learning Enabled Semantic Communications with Speech Recognition and Synthesis
Zhenzi Weng, Zhijin Qin, Xiaoming Tao +3
In this paper, we develop a deep learning based semantic communication system for speech transmission, named DeepSC-ST. We take the speech recognition and speech synthesis as the t…
FedGiA: An Efficient Hybrid Algorithm for Federated Learning
Shenglong Zhou, Geoffrey Ye Li
Federated learning has shown its advances recently but is still facing many challenges, such as how algorithms save communication resources and reduce computational costs, and whet…
Wireless Semantic Communications for Video Conferencing
Peiwen Jiang, Chao-Kai Wen, Shi Jin +1
Video conferencing has become a popular mode of meeting even if it consumes considerable communication resources. Conventional video compression causes resolution reduction under l…
Federated Learning via Inexact ADMM
Shenglong Zhou, Geoffrey Ye Li
One of the crucial issues in federated learning is how to develop efficient optimization algorithms. Most of the current ones require full device participation and/or impose strong…
Efficient Wireless Federated Learning with Partial Model Aggregation
Zhixiong Chen, Wenqiang Yi, Arumugam Nallanathan +1
The data heterogeneity across devices and the limited communication resources, e.g., bandwidth and energy, are two of the main bottlenecks for wireless federated learning (FL). To…
Low-Complexity Multicast Beamforming for Millimeter Wave Communications
Zhaohui Li, Chenhao Qi, Geoffrey Ye Li
To develop a low-complexity multicast beamforming method for millimeter wave communications, we first propose a channel gain estimation method in this article. We use the beam swee…