467 citations · 3.3k across the 111 of their papers we have counts for
14 papers · 1 filter
Communication and Localization with Extremely Large Lens Antenna Array
Jie Yang, Yong Zeng, Shi Jin +2
Achieving high-rate communication with accurate localization and wireless environment sensing has emerged as an important trend of beyond-fifth and sixth generation cellular system…
Meta Learning-based MIMO Detectors: Design, Simulation, and Experimental Test
Jing Zhang, Yunfeng He, Yu-Wen Li +2
Deep neural networks (NNs) have exhibited considerable potential for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. We…
Adaptive Bit Partitioning for Reconfigurable Intelligent Surface Assisted FDD Systems with Limited Feedback
Weicong Chen, Chao-Kai Wen, Xiao Li +1
In frequency division duplexing systems, the base station (BS) acquires downlink channel state information (CSI) via channel feedback, which has not been adequately investigated in…
Deep Learning-Based CSI Feedback for Beamforming in Single- and Multi-cell Massive MIMO Systems
Jiajia Guo, Chao-Kai Wen, Shi Jin
The potentials of massive multiple-input multiple-output (MIMO) are all based on the available instantaneous channel state information (CSI) at the base station (BS). Therefore, th…
Deep Learning for Joint Channel Estimation and Feedback in Massive MIMO Systems
Jiajia Guo, Tong Chen, Shi Jin +3
The great potentials of massive Multiple-Input Multiple-Output (MIMO) in Frequency Division Duplex (FDD) mode can be fully exploited when the downlink Channel State Information (CS…
Model-Driven Deep Learning for Massive MU-MIMO with Finite-Alphabet Precoding
Hengtao He, Mengjiao Zhang, Shi Jin +2
Massive multiuser multiple-input multiple-output (MU-MIMO) has been the mainstream technology in fifth-generation wireless systems. To reduce high hardware costs and power consumpt…
Model-Driven Deep Learning for Massive Multiuser MIMO Constant Envelope Precoding
Yunfeng He, Hengtao, He +2
Constant envelope (CE) precoding design is of great interest for massive multiuser multi-input multi-output systems because it can significantly reduce hardware cost and power cons…
Model-Driven DNN Decoder for Turbo Codes: Design, Simulation and Experimental Results
Yunfeng He, Jing Zhang, Shi Jin +2
This paper presents a novel model-driven deep learning (DL) architecture, called TurboNet, for turbo decoding that integrates DL into the traditional max-log-maximum a posteriori (…
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach
Hengtao He, Rui Wang, Weijie Jin +3
Millimeter-wave (mmWave) communications have been one of the promising technologies for future wireless networks that integrate a wide range of data-demanding applications. To comp…
Lightweight Convolutional Neural Networks for CSI Feedback in Massive MIMO
Zheng Cao, Wan-Ting Shih, Jiajia Guo +2
In frequency division duplex mode of massive multiple-input multiple-output systems, the downlink channel state information (CSI) must be sent to the base station (BS) through a fe…
MIMO Detection for Reconfigurable Intelligent Surface-Assisted Millimeter Wave Systems
Xi Yang, Chao-Kai Wen, Shi Jin
Millimeter wave (mmWave) band, or high frequencies such as THz, has large undeveloped band of spectrum. However, wireless channels over the mmWave band usually have one or two path…
Deep Learning-based CSI Feedback for RIS-assisted Multi-user Systems
Jiajia Guo, Xi Yang, Chao-Kai Wen +2
In the realm of reconfigurable intelligent surface (RIS)-assisted wireless communications, efficient channel state information (CSI) feedback is paramount. This paper introduces RI…
Adversarial Attack on DL-based Massive MIMO CSI Feedback
Qing Liu, Jiajia Guo, Chao-Kai Wen +1
With the increasing application of deep learning (DL) algorithms in wireless communications, the physical layer faces new challenges caused by adversarial attack. Such attack has s…
Deep Learning Based FDD Non-Stationary Massive MIMO Downlink Channel Reconstruction
Yu Han, Mengyuan Li, Shi Jin +2
This paper proposes a model-driven deep learning-based downlink channel reconstruction scheme for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems…