467 citations · 3.3k across the 111 of their papers we have counts for
24 papers · 1 filter
Low-Complexity Joint Beamforming for RIS-Assisted MU-MISO Systems Based on Model-Driven Deep Learning
Weijie Jin, Jing Zhang, Chao-Kai Wen +3
Reconfigurable intelligent surfaces (RIS) can improve signal propagation environments by adjusting the phase of the incident signal. However, optimizing the phase shifts jointly wi…
Semantic Communications using Foundation Models: Design Approaches and Open Issues
Peiwen Jiang, Chao-Kai Wen, Xinping Yi +3
Foundation models (FMs), including large language models, have become increasingly popular due to their wide-ranging applicability and ability to understand human-like semantics. W…
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…
Gradient-Based Markov Chain Monte Carlo for MIMO Detection
Xingyu Zhou, Le Liang, Jing Zhang +2
Accurately detecting symbols transmitted over multiple-input multiple-output (MIMO) wireless channels is crucial in realizing the benefits of MIMO techniques. However, optimal MIMO…
Communication-efficient Personalized Federated Edge Learning for Massive MIMO CSI Feedback
Yiming Cui, Jiajia Guo, Chao-Kai Wen +1
Deep learning (DL)-based channel state information (CSI) feedback has received significant research attention in recent years. However, previous research has overlooked the potenti…
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…
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…
Multi-task Learning-based CSI Feedback Design in Multiple Scenarios
Xiangyi Li, Jiajia Guo, Chao-Kai Wen +3
For frequency division duplex systems, the essential downlink channel state information (CSI) feedback includes the links of compression, feedback, decompression and reconstruction…
Deep Source-Channel Coding for Sentence Semantic Transmission with HARQ
Peiwen Jiang, Chao-Kai Wen, Shi Jin +1
Recently, semantic communication has been brought to the forefront because of its great success in deep learning (DL), especially Transformer. Even if semantic communication has be…
Deep Learning-based Implicit CSI Feedback in Massive MIMO
Muhan Chen, Jiajia Guo, Chao-Kai Wen +3
Massive multiple-input multiple-output can obtain more performance gain by exploiting the downlink channel state information (CSI) at the base station (BS). Therefore, studying CSI…
Adaptive Channel Estimation Based on Model-Driven Deep Learning for Wideband mmWave Systems
Weijie Jin, Hengtao He, Chao-Kai Wen +2
Channel estimation in wideband millimeter-wave (mmWave) systems is very challenging due to the beam squint effect. To solve the problem, we propose a learnable iterative shrinkage…
Fast Antenna and Beam Switching Method for mmWave Handsets with Hand Blockage
Wan-Ting Shih, Chao-Kai Wen, Shang-Ho +2
Many operators have been bullish on the role of millimeter-wave (mmWave) communications in fifth-generation (5G) mobile broadband because of its capability of delivering extreme da…
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…
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…
Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis
Jiajia Guo, Chao-Kai Wen, Shi Jin +1
Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency. However, these benefits are based on available channel stat…
Gridless Variational Bayesian Channel Estimation for Antenna Array Systems with Low Resolution ADCs
Jiang Zhu, Chao-kai Wen, Jun Tong +2
Employing low-resolution analog-to-digital converters (ADCs) coupled with large antenna arrays at the receivers has drawn considerable interests in the millimeter wave (mm-wave) sy…
Expectation Propagation Detector for Extra-Large Scale Massive MIMO
Hanqing Wang, Alva Kosasih, Chao-Kai Wen +2
The order-of-magnitude increase in the dimension of antenna arrays, which forms extra-large-scale massive multiple-input-multiple-output (MIMO) systems, enables substantial improve…
TurboNet: A Model-driven DNN Decoder Based on Max-Log-MAP Algorithm for Turbo Code
Yunfeng He, Jing Zhang, Chao-Kai Wen +1
This paper presents TurboNet, a novel model-driven deep learning (DL) architecture for turbo decoding that combines DL with the traditional max-log-maximum a posteriori (MAP) algor…
Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM
Jing Zhang, Hengtao He, Chao-Kai Wen +2
Channel estimation and signal detection are very challenging for an orthogonal frequency division multiplexing (OFDM) system without cyclic prefix (CP). In this article, deep learn…
AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results
Peiwen Jiang, Tianqi Wang, Bin Han +5
Orthogonal frequency division multiplexing (OFDM) has been widely applied in current communication systems. The artificial intelligence (AI)-aided OFDM receivers are currently brou…
ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers
Xuanxuan Gao, Shi Jin, Chao-Kai Wen +1
In this article, we propose a model-driven deep learning (DL) approach that combines DL with the expert knowledge to replace the existing orthogonal frequency-division multiplexing…
Implementation of Massive MIMO Uplink Receiver on RaPro Prototyping Platform
Xuanxuan Gao, Zhichao Huang, Xintong Lu +3
The updated physical layer standard of the fifth generation wireless communication suggests the necessity of a rapid prototyping platform. To this end, we develop RaPro, a multi-co…