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A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systems Journal article
Zhou, Binggui, Yang, Xi, Wang, Jintao, Ma, Shaodan, Gao, Feifei, Yang, Guanghua. A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systems[J]. IEEE Transactions on Wireless Communications, 2024, 23(10), 14743-14758.
Authors:  Zhou, Binggui;  Yang, Xi;  Wang, Jintao;  Ma, Shaodan;  Gao, Feifei; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:8.9/8.6 | Submit date：2024/08/05
Aigc  Csi Feedback  Domain Knowledge  Few-shot Learning  Massive Mimo  
Vision-aided Multi-user Beam Tracking for mmWave Massive MIMO System: Prototyping and Experimental Results Conference paper
Li, Kehui, Zhou, Binggui, Guo, Jiajia, Yang, Xi, Xue, Qing, Gao, Feifei, Ma, Shaodan. Vision-aided Multi-user Beam Tracking for mmWave Massive MIMO System: Prototyping and Experimental Results[C]:Institute of Electrical and Electronics Engineers Inc., 2024.
Authors:  Li, Kehui;  Zhou, Binggui;  Guo, Jiajia;  Yang, Xi;  Xue, Qing; et al.
Favorite | TC[Scopus]:0 | Submit date：2024/11/05
Beam Tracking  Computer Vision  Deep Learning  Massive Mimo  Prototype System  Training  Visualization  Vehicular And Wireless Technologies  Accuracy  Prototypes  Massive Mimo  
Natural Language Processing for Smart Healthcare Journal article
Zhou, Binggui, Yang, Guanghua, Shi, Zheng, Ma, Shaodan. Natural Language Processing for Smart Healthcare[J]. IEEE Reviews In Biomedical Engineering, 2024, 17, 4-18.
Authors:  Zhou, Binggui;  Yang, Guanghua;  Shi, Zheng;  Ma, Shaodan
Favorite | TC[WOS]:77 TC[Scopus]:52  IF:17.2/0 | Submit date：2023/01/30
Natural Language Processing  Smart Healthcare  Artificial Intelligencence  Nlp Techniques  Healthcare Applications  
Gridless Hybrid-Field Channel Estimation for Extra-Large Aperture Array Massive MIMO Systems Journal article
Xi, Yang, Zhu, Fuqiang, Zhou, Binggui, Liu, Ting, Ma, Shaodan. Gridless Hybrid-Field Channel Estimation for Extra-Large Aperture Array Massive MIMO Systems[J]. IEEE Wireless Communications Letters, 2024, 13(2), 496-500.
Authors:  Xi, Yang;  Zhu, Fuqiang;  Zhou, Binggui;  Liu, Ting;  Ma, Shaodan
Favorite | TC[WOS]:1 TC[Scopus]:2  IF:4.6/4.9 | Submit date：2024/02/22
Channel Estimation  Elaa Massive Mimo  Fractional Fourier Transform  Hybrid Field  Normalized Mean-squared Error  
Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots Journal article
Zhou, Binggui, Yang, Xi, Ma, Shaodan, Gao, Feifei, Yang, Guanghua. Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots[J]. IEEE Transactions on Wireless Communications, 2024, 24(6), 6061-6076.
Authors:  Zhou, Binggui;  Yang, Xi;  Ma, Shaodan;  Gao, Feifei;  Yang, Guanghua
Favorite | TC[WOS]:1 TC[Scopus]:1  IF:8.9/8.6 | Submit date：2024/02/22
Low-overhead Channel Estimation  Channel Estimation  Antenna Arrays  Attention Mechanism  Deep Learning  Estimation  Feature Extraction  Massive Mimo  Massive Mimo  Training  Transfer Learning  Wireless Communication  
BLER Analysis and Optimal Power Allocation of HARQ-IR for Mission-Critical IoT Communications Journal article
He, Fuchao, Shi, Zheng, Zhou, Binggui, Yang, Guanghua, Li, Xiaofan, Ye, Xinrong, Ma, Shaodan. BLER Analysis and Optimal Power Allocation of HARQ-IR for Mission-Critical IoT Communications[J]. IEEE Internet of Things Journal, 2024.
Authors:  He, Fuchao;  Shi, Zheng;  Zhou, Binggui;  Yang, Guanghua;  Li, Xiaofan; et al.
Favorite | TC[WOS]:0 TC[Scopus]:1  IF:8.2/9.0 | Submit date：2024/09/03
Block Error Rate  Decoding  Deep Reinforcement Learning  Fading Channels  Harq-ir  Internet Of Things  Markov Decision Process  Reliability Theory  Resource Management  Short Packet Communications  Signal To Noise Ratio  Throughput  
A novel model for tourism demand forecasting with spatial–temporal feature enhancement and image-driven method Journal article
Dong, Yunxuan, Zhou, Binggui, Yang, Guanghua, Hou, Fen, Hu, Zheng, Ma, Shaodan. A novel model for tourism demand forecasting with spatial–temporal feature enhancement and image-driven method[J]. NEUROCOMPUTING, 2023, 556, 126663.
Authors:  Dong, Yunxuan;  Zhou, Binggui;  Yang, Guanghua;  Hou, Fen;  Hu, Zheng; et al.
Favorite | TC[WOS]:4 TC[Scopus]:5  IF:5.5/5.5 | Submit date：2023/08/30
Deep Learning  Feature Enhancement  Spatial Series To Image Series  Spatial–temporal Learning  Tourism Demand Forecasting  
A graph-attention based spatial-temporal learning framework for tourism demand forecasting Journal article
Zhou, Binggui, Dong, Yunxuan, Yang, Guanghua, Hou, Fen, Hu, Zheng, Xu, Suxiu, Ma, Shaodan. A graph-attention based spatial-temporal learning framework for tourism demand forecasting[J]. Knowledge-Based Systems, 2023, 263, 110275.
Authors:  Zhou, Binggui;  Dong, Yunxuan;  Yang, Guanghua;  Hou, Fen;  Hu, Zheng; et al.
Favorite | TC[WOS]:6 TC[Scopus]:8  IF:7.2/7.4 | Submit date：2023/04/03
Tourism Demand Forecasting  Dynamic Spatial Connections  Spatial-temporal Learning  Graph Neural Network  Attention Mechanism  
Transformer-based CSI Feedback with Hybrid Learnable Non-Uniform Quantization for Massive MIMO Systems Conference paper
Zhou,Binggui, Ma,Shaodan, Yang,Guanghua. Transformer-based CSI Feedback with Hybrid Learnable Non-Uniform Quantization for Massive MIMO Systems[C]:Institute of Electrical and Electronics Engineers Inc., 2023.
Authors:  Zhou,Binggui;  Ma,Shaodan;  Yang,Guanghua
Favorite | TC[Scopus]:2 | Submit date：2023/08/03
Csi Feedback  Deep Learning  Massive Mimo  Quantization  Transformer  
Interpretable Temporal Attention Network for COVID-19 forecasting Journal article
Zhou, Binggui, Yang, Guanghua, Shi, Zheng, Ma, Shaodan. Interpretable Temporal Attention Network for COVID-19 forecasting[J]. Applied Soft Computing, 2022, 120, 108691.
Authors:  Zhou, Binggui;  Yang, Guanghua;  Shi, Zheng;  Ma, Shaodan
Favorite | TC[WOS]:20 TC[Scopus]:26  IF:7.2/7.0 | Submit date：2022/05/13
Covariate Forecasting  Covid-19 Forecasting  Degraded Teacher Forcing  Multi-task Learning  Neural Network