📝 Publications
Representative Publications (by Topic) | List of Publications (by Topic)
Representative Publications (by Topic)
(† for Equal Contribution; * for Corresponding Authorship.)
Wireless Communications
Binggui Zhou, Xi Yang, Shaodan Ma, Feifei Gao, and Guanghua Yang, “Low-Overhead Channel Estimation via 3D Extrapolation for TDD mmWave Massive MIMO Systems Under High-Mobility Scenarios,” IEEE Transactions on Wireless Communications, vol. 24, no. 4, pp. 2797-2813, Apr. 2025. (JCR Q1, IF: 8.9)
Preprint |
- In this work, we propose a three-domain (3D) channel extrapolation framework across spatial, frequency, and temporal domains to reduce pilot training overhead for TDD mmWave massive MIMO systems under high mobility. The proposed method first realizes uplink channel estimation with the knowledge-and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) to reduce the spatial-frequency domain pilot training overhead $C_{sl}$, and then conducts accurate slot-level channel extrapolation with the temporal uplink-downlink channel extrapolation network (TUDCEN) to reduce the times of uplink channel estimations $\frac{T}{T_p}$, thereby systematically reducing the pilot training overhead $C_o$.
- Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by 16 times and improving the system’s spectral efficiency under high-mobility scenarios compared with state-of-the-art channel estimation/extrapolation methods.
Binggui Zhou, Xi Yang, Jintao Wang, Shaodan Ma, Feifei Gao, and Guanghua Yang, “A Low-Overhead Incorporation-Extrapolation Based Few-Shot CSI Feedback Framework for Massive MIMO Systems,” IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 14743-14758, Oct. 2024. (JCR Q1, IF: 8.9)
Preprint |
- In this work, we propose the Incorporation-Extrapolation based Few-Shot CSI feedback Framework (IEFSF) for massive MIMO systems to enable low-overhead CSI feedback with reduced data collection cost. An incorporation-extrapolation scheme for eigenvector-based CSI feedback is proposed to reduce the feedback overhead. Then, to alleviate the necessity of extensive collected samples and enable few-shot CSI feedback, we further propose a knowledge-driven data augmentation (KDDA) method and an artificial intelligence-generated content (AIGC) -based data augmentation method by exploiting the domain knowledge of wireless channels and by exploiting a novel generative model, respectively.
- Experimental results based on the DeepMIMO dataset demonstrate that the proposed IEFSF significantly reduces CSI feedback overhead by 64 times compared with existing methods while maintaining higher feedback accuracy using only several hundred collected samples.
Binggui Zhou, Xi Yang, Shaodan Ma, Feifei Gao, and Guanghua Yang, “Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots,” IEEE Transactions on Wireless Communications, vol. 23, no. 6, pp. 6061-6076, Jun. 2024. (JCR Q1, IF: 8.9)
- In this work, we propose the dual-attention-based channel estimation network (DACEN) to realize accurate channel estimation via low-density pilots, by jointly learning the spatial-temporal domain features of massive MIMO channels with the temporal attention module and the spatial attention module. To further improve the estimation accuracy, we propose a parameter-instance transfer learning approach to transfer the channel knowledge learned from the high-density pilots pre-acquired during the training dataset collection period.
- Experimental results reveal that the proposed DACEN-based method can reduce up to 92% of pilot overhead by reducing the pilot density from 26/52 to 2/52 than traditional channel estimation methods. Additionally, with the proposed parameter-instance transfer learning approach, the DACEN-based method achieves additional performance gain, thereby further demonstrating the effectiveness and superiority of the proposed method.
Machine Learning and Data Science
A Graph-Attention Based Spatial-Temporal Learning Framework for Tourism Demand Forecasting
Binggui Zhou, Yunxuan Dong, Guanghua Yang, Fen Hou, Zheng Hu, Suxiu Xu, and Shaodan Ma, “A Graph-Attention Based Spatial-Temporal Learning Framework for Tourism Demand Forecasting,” Knowledge-Based Systems, vol. 263, p. 110275, Mar. 2023. (JCR Q1, IF: 7.2)
- In this paper, we propose a graph-attention based spatial–temporal learning framework for tourism demand forecasting. A weight-dynamic multi-dimensional graph is organized to embed multiple explicit dynamic spatial connections and provide a node attribute sequence for learning implicit dynamic spatial connections. We further propose a heterogeneous spatial–temporal graph-attention network (HSTGANet), which is effective in handling both explicit and implicit dynamic spatial connections, learning high-dimensional spatial–temporal features, and forecasting tourism demand.
- Experimental results demonstrate the effectiveness of the proposed model over baseline models in forecasting the tourism demand for six regions of Wanshan Archipelago in Zhuhai, China, and indicate that the proposed spatial–temporal learning framework may provide useful insights for developing more effective models for other spatial–temporal forecasting problems.
Interpretable Temporal Attention Network for COVID-19 Forecasting
Binggui Zhou, Guanghua Yang, Zheng Shi, and Shaodan Ma, “Interpretable Temporal Attention Network for COVID-19 Forecasting,” Applied Soft Computing, vol. 120, p. 108691, May 2022. (JCR Q1, IF: 7.2)
- In this work, we propose the Interpretable Temporal Attention Network (ITANet) for COVID-19 forecasting and inferring the importance of government interventions. The proposed model is with an encoder–decoder architecture and employs long short-term memory (LSTM) for temporal feature extraction and multi-head attention for long-term dependency caption. The model simultaneously takes historical information, a priori known future information, and pseudo future information into consideration, where the pseudo future information is learned with the covariate forecasting network (CFN) and multi-task learning (MTL). In addition, we also propose the degraded teacher forcing (DTF) method to train the model efficiently.
- Compared with other models, the ITANet is more effective in the forecasting of COVID-19 new confirmed cases.
- The importance of government interventions against COVID-19 is further inferred by the Temporal Covariate Interpreter (TCI) of the model.
Smart Healthcare
Natural Language Processing for Smart Healthcare
Binggui Zhou, Guanghua Yang, Zheng Shi, and Shaodan Ma, “Natural Language Processing for Smart Healthcare,” IEEE Reviews in Biomedical Engineering, vol. 17, pp. 4-18, Jan. 2024. (JCR Q1, IF: 17.2, ESI Hot Paper, ESI Highly Cited Paper, and Popular Article & Featured Article of IEEE RBME)
Preprint |
- In this work, we review existing studies that concern NLP for smart healthcare from the perspectives of technique and application.
- We first elaborate on different NLP approaches and the NLP pipeline for smart healthcare from the technical point of view. Then, in the context of smart healthcare applications employing NLP techniques, we introduce several representative smart healthcare scenarios. We further discuss two specific medical issues, i.e., the coronavirus disease 2019 (COVID-19) pandemic and mental health, in which NLP-driven smart healthcare plays an important role. Finally, we discuss the limitations of current works and identify the directions for future works.
List of Publications (by Topic)
(† for Equal Contribution; * for Corresponding Authorship; J for Journal Publications; C for Conference Publications; P for Patent Grants/Applications.)
Wireless Communications
[J16] Yiyang Peng, Binggui Zhou, Yutong Zheng, Danilo Mandic, and Bruno Clerckx, “Joint Training Scattering Matrix Learning and Channel Estimation for Beyond-Diagonal Reconfigurable Intelligent Surfaces,” submitted to IEEE Transactions on Wireless Communications. Preprint
[J15] Qikai Xiao†, Kehui Li†, Binggui Zhou, and Shaodan Ma, “Deep Learning Based Channel Extrapolation for Dual-Band Massive MIMO Systems,” submitted to IEEE Wireless Communications Letters. Preprint
[J14] Binggui Zhou and Bruno Clerckx, “Beyond-Diagonal RIS Under Non-Idealities: Learning-Based Architecture Discovery and Optimization,” submitted to IEEE Transactions on Wireless Communications. Preprint
[J13] Xi Yang*, Dahong Du, Ting Liu, Binggui Zhou*, and Shaodan Ma, “Channel Recovery for UPA-assisted Massive MlMO Systems with Asymmetrical Uplink and Downlink Transceivers,” IEEE Internet of Things Journal, vol. 13, no. 7, pp. 14829-14843, Apr. 2026. (JCR Q1, IF: 8.2) Published Version
[J12] Hao Xia, Qing Xue, Yanping Liu, Binggui Zhou, Meng Hua, and Qianbin Chen, “Intelligent Angle Map-based Beam Alignment for RIS-aided mmWave Communication Networks,” IEEE Transactions on Network Science and Engineering, vol. 13, pp. 5833-5850, 2026. (JCR Q1, IF: 8.3) Published Version | Preprint
[J11] Kehui Li, Binggui Zhou*, Jiajia Guo, Feifei Gao, Guanghua Yang*, and Shaodan Ma, “Out-of-Band Modality Synergy Based Multi-User Beam Prediction and Proactive BS Selection with Zero Pilot Overhead”, IEEE Transactions on Communications, vol. 74, pp. 3858-3874, 2026. (JCR Q1, IF: 8.3) Published Version | Preprint
[J10] Jintao Wang, Binggui Zhou, Chengzhi Ma, Shiqi Gong, Guanghua Yang, and Shaodan Ma, “Robust Beamforming Design and Antenna Selection for Dynamic HRIS-aided MISO Systems,” IEEE Transactions on Vehicular Technology, vol. 74, no. 9, pp. 14943-14948, Sep. 2025. (JCR Q1, IF: 6.1) Published Version | Preprint
[J9] Binggui Zhou, Xi Yang, Shaodan Ma, Feifei Gao, and Guanghua Yang, “Low-Overhead Channel Estimation via 3D Extrapolation for TDD mmWave Massive MIMO Systems Under High-Mobility Scenarios,” IEEE Transactions on Wireless Communications, vol. 24, no. 4, pp. 2797-2813, Apr. 2025. (JCR Q1, IF: 8.9) Published Version | Preprint
[J8] Jianpeng Zou, Zheng Shi, Binggui Zhou, Yaru Fu, Hong Wang, and Weiqiang Tan, “Throughput Maximization of HARQ-IR for ISAC,” IEEE Communications Letters, vol. 29, no. 3, pp. 492-496, Mar. 2025. (JCR Q2, IF: 3.7) Published Version
[J7] Qing Xue, Jiajia Guo, Binggui Zhou, Yongjun Xu, Zhidu Li, and Shaodan Ma, “AI/ML for Beam Management in 5G-Advanced: A Standardization Perspective,” IEEE Vehicular Technology Magazine, vol. 19, no. 4, pp. 64-72, Dec. 2024. (JCR Q1, IF: 5.8) Published Version | Preprint
[J6] Fuchao He, Zheng Shi, Binggui Zhou, Guanghua Yang, Xiaofan Li, Xinrong Ye, and Shaodan Ma, “BLER Analysis and Optimal Power Allocation of HARQ-IR for Mission-Critical IoT Communications,” IEEE Internet of Things Journal, vol. 11, no. 21, pp. 35536-35550, 1 Nov.1, 2024. (JCR Q1, IF: 8.2) Published Version
[J5] Binggui Zhou, Xi Yang, Jintao Wang, Shaodan Ma, Feifei Gao, and Guanghua Yang, “A Low-Overhead Incorporation-Extrapolation Based Few-Shot CSI Feedback Framework for Massive MIMO Systems,” IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 14743-14758, Oct. 2024. (JCR Q1, IF: 8.9) Published Version | Preprint
[J4] Binggui Zhou, Xi Yang, Shaodan Ma, Feifei Gao, and Guanghua Yang, “Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots,” IEEE Transactions on Wireless Communications, vol. 23, no. 6, pp. 6061-6076, Jun. 2024. (JCR Q1, IF: 8.9) Published Version | Preprint | Code
[J3] Xi Yang*, Fuqiang Zhu, Binggui Zhou*, Ting Liu, and Shaodan Ma, “Gridless Hybrid-Field Channel Estimation for Extra-Large Aperture Array Massive MIMO Systems,” IEEE Wireless Communications Letters, vol. 13, no. 2, pp. 496-500, Feb. 2024. (JCR Q1, IF: 4.6) Published Version
[J2] Xianda Wu, Xi Yang, Shaodan Ma, Binggui Zhou, and Guanghua Yang, “Hybrid Channel Estimation for UPA-Assisted Millimeter-Wave Massive MIMO IoT Systems,” IEEE Internet of Things Journal, vol. 9, no. 4, pp. 2829-2842, Feb. 2022. (JCR Q1, IF: 8.2) Published Version
[J1] Xiaohong Chen, Changxing Deng, Binggui Zhou, Huan Zhang, Shaodan Ma, and Guanghua Yang, “High-Accuracy CSI Feedback with Super-Resolution Network for Massive MIMO Systems,” IEEE Wireless Communications Letters, vol. 11, no. 1, pp. 141-145, Jan. 2022. (JCR Q1, IF: 4.6) Published Version | Code
[C3] Weiqiang Tan, Minwei Zhang, Jintao Wang, Binggui Zhou, Xiyuan Chen, and Chunguo Li, “Sequence-Model-Based Joint CSI Feedback and Dynamic Multiuser Precoding for FDD Massive MIMO Systems,“ in Proceedings of IEEE INFOCOM WKSHPS, pp. 1-6, 2026.
[C2] Kehui Li, Binggui Zhou, Jiajia Guo, Xi Yang, Qing Xue, Feifei Gao, and Shaodan Ma, “Vision-aided Multi-user Beam Tracking for mmWave Massive MIMO System: Prototyping and Experimental Results,” in Proceedings of IEEE Vehicular Technology Conference: VTC2024-Spring, pp. 1-6, 2024.
[C1] Binggui Zhou, Shaodan Ma, and Guanghua Yang, “Transformer-based CSI Feedback with Hybrid Learnable Non-Uniform Quantization for Massive MIMO Systems,” in Proceedings of 2023 32nd Wireless and Optical Communications Conference (WOCC), pp. 1-5, 2023.
[P3] Shaodan Ma, Xi Yang, Chengzhi Ma, Binggui Zhou, and Jintao Wang. “An Enhanced Distributed Hybrid RIS Based Wireless Communication System,” Chinese Patent Application, Sep. 2024.
[P2] Shaodan Ma, Xi Yang, Chengzhi Ma, Binggui Zhou, and Jintao Wang. “A Distributed Hybrid RIS Based Wireless Communication System,” Chinese Patent Grant, CN116056118B, Mar. 2025.
[P1] Shaodan Ma, Changxing Deng, Xiaohong Chen, Huan Zhang, and Binggui Zhou. “Compression Methods, Reconstruction Methods, Devices, and Computer Equipments for Channel State Information,” Chinese Patent Grant, CN113938952B, Oct. 2023.
Machine Learning and Data Science
[J5] Yunxuan Dong, Binggui Zhou, Hongcai Zhang, Guanghua Yang, and Shaodan Ma, “A Deep Time-Frequency Augmented Wind Power Forecasting Model,” Renewable Energy, vol. 256, p. 123550, Jan. 2026. (JCR Q1, IF: 9.0) Published Version
[J4] Binggui Zhou, Guanghua Yang, Zheng Shi, and Shaodan Ma, “Natural Language Processing for Smart Healthcare,” IEEE Reviews in Biomedical Engineering, vol. 17, pp. 4-18, Jan. 2024. (JCR Q1, IF: 17.2, ESI Hot Paper, ESI Highly Cited Paper, and Popular Article & Featured Article of IEEE Reviews in Biomedical Engineering) Published Version | Preprint
[J3] Yunxuan Dong†, Binggui Zhou†, Guanghua Yang, Fen Hou, Zheng Hu, and Shaodan Ma, “A Novel Model for Tourism Demand Forecasting with Spatial–Temporal Feature Enhancement and Image-Driven Method,” Neurocomputing, vol. 556, p. 126663, Nov. 2023. (JCR Q1, IF: 6.0) Published Version
[J2] Binggui Zhou, Yunxuan Dong, Guanghua Yang, Fen Hou, Zheng Hu, Suxiu Xu, and Shaodan Ma, “A Graph-Attention Based Spatial-Temporal Learning Framework for Tourism Demand Forecasting,” Knowledge-Based Systems, vol. 263, p. 110275, Mar. 2023. (JCR Q1, IF: 7.2) Published Version
[J1] Binggui Zhou, Guanghua Yang, Zheng Shi, and Shaodan Ma, “Interpretable Temporal Attention Network for COVID-19 Forecasting,” Applied Soft Computing, vol. 120, p. 108691, May 2022. (JCR Q1, IF: 7.2) Published Version
[C2] Yunxuan Dong, Binggui Zhou, Guanghua Yang, Fen Hou, and Shaodan Ma, “A Spatial-temporal Model for Tourism Demand Forecasting,” in Proceedings of 2021 IEEE 19th Int Conf on Smart City (SmartCity), pp. 1810-1814, 2021.
[C1] Nan Lin, Binggui Zhou, Guanghua Yang, and Shaodan Ma, “Multi-head Attention Networks for Nonintrusive Load Monitoring,” in Proceedings of 2020 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), pp. 1-5, 2020.
[P1] Binggui Zhou, Guanghua Yang, Zheng Shi, Suxiu Xu, Zheng Hu, and Haitao Jiang. “A Spatial-temporal Tourism Demand Forecasting Method Based on Graph Convolution and Attention Mechanism,” Chinese Patent Grant, CN115438837B, Sep. 2025.
Miscellaneous
[J1] Binggui Zhou, Qingkai Liu, and Ju Qiu. “Indoor IoT Security System Based on Raspberry Pi and WeChat,” Transducer and Microsystem Technologies, vol. 36, no. 11, pp. 109-111+122, Nov. 2017. (Chinese Science Citation Database (CSCD))
[C1] Binggui Zhou, Guanghua Yang, and Shaodan Ma, “Product-oriented Product Service System for Large-scale Vision Inspection,” in Procedia CIRP, vol. 83, pp. 675-679, 2019.