Binggui Zhou

Binggui Zhou

Postdoc@ICL

Binggui Zhou (周炳贵) received his B.Eng. degree in Electrical Engineering from Jinan University, Zhuhai, China, in 2018, and his M.Sc. degree and Ph.D. degree in Electrical and Computer Engineering from the University of Macau, Macao, China, in 2021 and 2024, respectively, with the State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC) and under the supervision of Prof. Shaodan Ma (SMIEEE, IEEE ComSoc Distinguished Lecturer). He is currently a Postdoctoral Research Associate with the Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom, under the guidance of Prof. Bruno Clerckx (FIEEE, FIET, IEEE ComSoc Distinguished Lecturer).

Dr. Zhou’s research interests include machine learning and data science as well as their applications in wireless communications and smart healthcare. He has published more than 20 papers in top journals and flagship international conferences (e.g., IEEE TWC, KNOSYS, RENE, IEEE IOT-J, IEEE TVT, etc.) with total citations. Dr. Zhou has served as a Technical Program Committee member for several flagship international conferences (e.g., IEEE VTC) and as a reviewer for multiple top-tier journals and flagship international conferences (e.g., IEEE WCM, IEEE CM, IEEE TWC, IEEE TMC, IEEE TCOM, KNOSYS, IPM, etc.).

🔥 News

💼 Professional Experience

  • Sep. 2024 - present, Postdoctoral Research Associate, Department of Electrical and Electronic Engineering, Imperial College London

🎓 Academic Qualifications

  • Aug. 2021 - Jul. 2024, Doctor of Philosophy in Electrical and Computer Engineering, State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC) and Department of Electrical and Computer Engineering, University of Macau

  • Aug. 2018 - Jun. 2021, Master of Science in Electrical and Computer Engineering, State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC) and Department of Electrical and Computer Engineering, University of Macau

  • Sep. 2014 - Jun. 2018, Bachelor of Engineering in Electrical Engineering, School of Electrical and Information Engineering, Jinan University

🌏 Academic Services

Wireless Communications

Conference TPC Member

  • IEEE VTC2025-Fall - Tracks: Signal Processing for Wireless Communications; Multiple Antennas and Cooperative Communications
  • IEEE VTC2025-Spring - Track: Multiple Antennas and Cooperative Communications
  • IEEE VTC2024-Spring - Track: Signal Transmission and Reception

Journal Referee

  • IEEE Wireless Communications (WCM)
  • IEEE Communications Magazine (CM)
  • IEEE Transactions on Wireless Communications (TWC)
  • IEEE Transactions on Mobile Computing (TMC)
  • IEEE Transactions on Communications (TCOM)
  • IEEE Internet of Things Journal (IoT-J)
  • IEEE Transactions on Cognitive Communications and Networking (TCCN)
  • IEEE Transactions on Vehicular Technology (TVT)
  • IEEE Wireless Communication Letters (WCL)

Conference Reviewer

  • IEEE International Conference on Communications (ICC)
  • IEEE Global Communications Conference (GLOBECOM)
  • IEEE Vehicular Technology Conference (VTC)
  • IEEE Asia-Pacific Conference on Communications (APCC)
  • IEEE/CIC International Conference on Communications in China (ICCC)

Standardization

  • Member of the University of Macau (Member Unit) Research Team within the Wireless Technology Group of the IMT-2030 (6G) Promotion Group founded by the Ministry of Industry and Information Technology (MIIT) of China

Machine Learning and Data Science

Journal Referee

  • Knowledge-Based Systems (KNOSYS)
  • Information Processing and Management (IPM)
  • Journal of Cleaner Production (JCLEPRO)
  • Journal of Supercomputing

Conference Reviewer

  • International Conference on Learning Representations (ICLR) Workshops
  • International Joint Conference on Neural Networks (IJCNN)

Smart Healthcare

Journal Referee

  • Computers in Biology and Medicine (CIBM)
  • Computer Methods in Biomechanics and Biomedical Engineering

Conference Reviewer

  • International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

🏆 Recognition and Awards

  • May 2023, Winning Prize of the First 6G Intelligent Wireless Communication System Competition (Ranking 9/1252), as the Captain
  • Apr. 2022, Second Prize of the First 6G AI Competition (Ranking 2/727), as the Captain
  • May 2018, Second Prize of the ASC Student Supercomputer Challenge, as the Captain

🔉 Invited Talks

  • Aug. 2024, “Knowledge-and-Data Driven CSI Estimation and Feedback for Massive MIMO Systems”, Guangxi University
  • Jul. 2024, “Knowledge-and-Data Driven CSI Estimation and Feedback for Massive MIMO Systems”, South China Normal University

📝 Publications

Representative Publications (by Topic) | List of Publications (by Topic)

Representative Publications (by Topic)

(† for Equal Contribution; * for Corresponding Authorship.)

Wireless Communications

IEEE TWC
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Low-Overhead Channel Estimation via 3D Extrapolation for TDD mmWave Massive MIMO Systems Under High-Mobility Scenarios

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.
IEEE TWC
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A Low-Overhead Incorporation-Extrapolation Based Few-Shot CSI Feedback Framework for Massive MIMO Systems

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.
IEEE TWC
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Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots

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)

Preprint | Code |

  • 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

KNOSYS
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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: 8.8)

  • 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.
ASOC
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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: 8.7)

  • 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

IEEE RBME
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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: 17.6, 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

[J11] Hao Xia, Qing Xue, Yanping Liu, Binggui Zhou, Meng Hua, Qianbin Chen, “Intelligent Angle Map-based Beam Alignment for RIS-aided mmWave Communication Networks,” submitted to IEEE Transactions on Network Science and Engineering, under review. 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,” accepted by IEEE Transactions on Vehicular Technology. (JCR Q1, IF: 6.1) Published | 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 | 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

[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 | 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

[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 | 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 | 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: 6.3) Published

[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: 10.6) Published

[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: 6.3) Published | Code

[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

[J4] Yunxuan Dong, Binggui Zhou, Hongcai Zhang, Guanghua Yang, and Shaodan Ma, “A Deep Time-Frequency Augmented Wind Power Forecasting Model,” accepted by Renewable Energy. (JCR Q1, IF: 9.0)

[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

[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: 8.8) Published

[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: 8.7) Published

[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 Application, Aug. 2022.

Smart Healthcare

[J1] 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: 17.6, ESI Hot Paper, ESI Highly Cited Paper, and Popular Article & Featured Article of IEEE Reviews in Biomedical Engineering) Published | Preprint

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.

🔎 Research

Research Interests | Research Highlights | Collaborators

Research Interests

Methods

  • Machine Learning: Deep Learning, Graph Machine Learning, Generative Artificial Intelligence

  • Data Science: Statistical Analysis, Data Efficiency, Correlation Mining

  • Natural Language Processing: Neural Natural Language Processing (Neural NLP); Knowledge Base/Graph

Applications

  • Wireless Communications: Knowledge and Data Driven Signal Processing in Massive/Extremely Large-Scale MIMO; Beyond Diagonal Reconfigurable Intelligent Surfaces (BD-RIS); Integrated Sensing and Communications

  • Applied Data Mining: Spatial-Temporal (Demand) Forecasting, Energy Disaggregation

  • Smart Healthcare: Health Informatics

Research Highlights

1. Knowledge-and-Data Driven Driven CSI Acquisition

Currently, mobile communications have evolved into 5G/B5G and we are now anticipating what 6G will be like. According to [1], the usage scenarios of IMT-2030 (6G) include immersive communication, hyper reliable and low-latency communication, massive communication, ubiquitous connectivity, integrated sensing and communication, etc. These usage scenarios raise high data rate requirements, hyper reliability requirements, and massive connection requirements to current wireless communication systems. To meet these requirements, extreme/massive MIMO has been recognized as an essential technology to provide spatial degrees of freedom, diversity or multiplexing gain, and array gain, thereby improving the spectral and energy efficiencies of wireless communication systems.

However, extreme/massive MIMO also brings out complicated channel characteristics and high overhead and computational complexity, which are significantly challenging for channel state information (CSI) acquisition. It is fortunate that by learning from extensive data, some deep learning-based algorithms have been proposed to capture the complicated channel characteristics of massive MIMO channels and further improve the spectral and energy efficiencies of massive MIMO systems. Nonetheless, the high overhead and computational complexity of massive MIMO systems are not significantly reduced by existing deep learning-based algorithms. In addition, most existing deep learning-based algorithms are purely data-driven and basically rely on extensive collected data for learning the features of complicated wireless channels, leading to unaffordable data collection costs.

To simultaneously improve spectral and energy efficiencies, reduce overhead and computational complexity, and circumvent extensive data collection towards accurate and efficient CSI acquisition, we investigate how to realize knowledge-and-data driven CSI estimation and feedback, particularly for massive MIMO systems. Both domain knowledge, e.g., multi-domain correlations and channel sparsity, and learning data, e.g., historical data and cross-domain data, are exploited for framework design, learning paradigm design, and computationally efficient design to achieve the aforementioned goals. Some frameworks and algorithms we proposed are listed above and the interested readers are referred to the following publications:

[1] 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.

[2] 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.

[3] 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 senarios,” IEEE Transactions on Wireless Communications, vol. 24, no. 4, pp. 2797-2813, Apr. 2025.

2. Machine Learning Empowered Wireless Communications: Prototyping and Demo Systems

Vision-aided multi-user sensing and communications prototyping system:

Details of the prototyping system can be found in our Lab Pages and the following publications.

[1] 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.

3. Pratical Verifications of Machine Learning Empowered Wireless Communications

(1) Check out Our Solution to the First 6G AI Competition held by the Guangdong OPPO Mobile Communications Corp., Ltd, which won the Second Prize and ranked 2/727 teams.

Channel modeling is an important area of 6G pre-research. To cope with the increasingly complex wireless communication environment and make full use of data-driven/knowledge-and-data driven algorithms for complex channel modeling, we propose a generative adversarial network (GAN) for channel modeling and constructing an extensive wireless channel dataset of high-quality samples using a small number of real channel samples. The proposed GAN is based on the multi-head self-attention mechanism and convolution operations. We decouple the channel generation problem into two parts: valid (meaningful) delayed path position generation and valid (meaningful) delayed path generation. Therefore, the generator contains two sub-networks. The path generation sub-network mainly exploits Transformer layers as the backbone, while the backbone of the path position generation sub-network is with a two-layer multi-layer perceptron structure. The discriminator exploits multiple convolutional downsampling modules to distinguish between generated samples and real samples.

(2) Our recent solution to CSI feedback with limited samples won the Winning Prize (ranking 9/1252 teams) of the First 6G Intelligent Wireless Communication System Competition held by the IMT-2030 (6G) Promotion Group and Guangdong OPPO Mobile Communications Corp., Ltd.

Deep learning based CSI feedback has received widespread attention from academia and industry in recent years. However, most of the existing deep learning based CSI feedback methods are purely data-driven. In addition to obtaining high-performance gains brought by data-driven, such methods also show poor generalization performance in different scenarios. Currently, to mitigate this issue, expensive data collection costs and long training time for different scenarios are inevitable, and thus poses further challenges for the implementation of such deep learning based CSI feedback methods. To address these challenges, we propose a CSI feedback method that uses only a small number of samples to obtain better generalization capabilities. Through frequency domain data augmentation and the advanced dual-attention-based CSI feedback model, the proposed CSI feedback method can achieve CSI feedback with good generalization ability in a very concise way. In addition, to mitigate quantization errors, we further propose a quantization ensemble framework which exploits several quantizers and dequantizers for ensemble. Specifically, constrained by 30-bit CSI feedback overhead and to balance the number of feedback bits, quantization error and the size of backbone networks, we use 27 bits for quantization and 3 bits as the quantizer index (which indicates that a total of 8 quantizers with different configurations can be utilized). Hybrid scalar quantization is considered, and each two quantizers share an Encoder backbone network to ensure that each Encoder backbone network has larger model scale and learning capabilities.

The dual-attention-based CSI feedback model (left) and the quantization ensemble framework (right):

Collaborators

I collaborate closely with Prof. Shaodan Ma (Professor, University of Macau), Prof. Guanghua Yang (Professor, Jinan University), Prof. Feifei Gao (Professor, Tsinghua University), Prof. Xi Yang (Professor, East China Normal University), Prof. Zheng Shi (Professor, Jinan University), Prof. Qing Xue (Associate Professor, Chongqing University of Posts and Telecommunications), and Dr. Jintao Wang (Postdoctoral Researcher, University of Macau) on wireless communications, and with Prof. Yunxuan Dong (Assistant Professor, Guangxi University) on applied data mining.

Feel free to drop me an email if you would like to collaborate with me.