郭佳(Guo, Jia)

2
Sep
2021
zhaobaichuan
郭佳, 博士研究生, 2016年9月入学    
Jia Guo, Ph.D. candidate, since Sept. 2016    
Email:     guojia@buaa.edu.cn    
Address: Group 203, Beihang University,    
Beijing, China 100191

郭佳，天津人，2019年1月于北京航空航天大学电子信息工程学院获硕士学位。同年9月进入北航电子信息工程学院攻读博士学位。主要研究方向为机器学习在无线通信中的应用。

Jia Guo received the B.S. degree in electronics engineering and the M.S. degree in information and communication engineering from Beihang University, China, in 2016 and 2019, respectively, where he is currently pursuing the Ph.D. degree in signal and information processing with the School of Electronics and Information Engineering. His research interests include the area of machine learning for wireless communications.

Publications

Journal Papers

  1. Jia Guo, and Chenyang Yang: A Model-Based GNN for Learning Precoding, IEEE Trans. Wireless Commun., 2023, Early Access.

  2. Yao Peng, Jia Guo and Chenyang Yang: Learning Resource Allocation Policy: Vertex-GNN or Edge-GNN?, IEEE Trans. Mach. Learn. Commun. Netw., vol. 2, pp. 190-209, Jan. 2024.

  3. Jia Guo, and Chenyang Yang: Deep Neural Networks with Data Rate Model: Learning Power Allocation Efficiently, IEEE Trans. Commun., 2023, 71(3): 1447-1461

  4. Jia Guo, and Chenyang Yang: Learning Power Allocation for Multi-cell-multi-user Systems with Heterogeneous Graph Neural Network, IEEE Trans. on Wireless Communications, 2022, 21(2): 884-897

  5. Jia Guo, Zhaoqi Xu and Chenyang Yang: Learning Fairly with Class-imbalanced Data for Wireless Tasks, IEEE Transactions on Vehicular Technology, IEEE Trans. On Vehicular Technology, vol. 70, no. 7, July 2021, pp. 7176-7181.

  6. Jia Guo and Chenyang Yang: Impact of Prediction Errors on High Throughput Predictive Resource Allocation, IEEE Transactions on Vehicular Technology, IEEE Trans. On Vehicular Technology, vol. 66, no. 9, pp. 9984 – 9999, Sept. 2020

  7. Jia Guo, Chenyang Yang and I Chih-Lin: Exploiting Future Radio Resources with End-to-end Prediction by Deep Learning, IEEE Access, vol. 6, pp. 75729-75747, 2018.

  8. Shengjie Liu, Jia Guo, and Chenyang Yang: Multidimensional Graph Neural Networks for Wireless Communications, IEEE Transactions on Wireless Communications, Early Access, 2023.

  9. Chenzuo Zhang, Jia Guo, and Chenyang Yang: When the Gain of Predictive Resource Allocation for Content Delivery is Large?, Science China Information Science, 2022, accepted.

  10. 郭佳，余永斌，杨晨阳：基于全注意力机制的多步网络流量预测，信号处理，2019，35(5)，758-767.

  11. 张宸祚，赵百川，徐兆祺，郭佳，杨晨阳：同构与异构网中预测资源分配的性能，信号处理，2019,35(10),1641-1651.

    Conference Papers

    1. Jia Guo and Chenyang Yang: A Size-Generalizable GNN for Learning Precoding, IEEE VTC Fall, 2023, accepted.

    2. Jia Guo and Chenyang Yang: How to Improve Learning Efficiency of GNN for Precoding? , IEEE VTC Spring, 2023.

    3. Jia Guo, Mehdi Bennis and Chenyang Yang: Precoder and Detector Learning for Vision-based mmWave Received Power Prediction, IEEE PIMRC, 2023.

    4. Jia Guo and Chenyang Yang: Learning Precoding for Semantic Communications, IEEE ICC Workshops, 2022.

    5. Jia Guo and Chenyang Yang: Learning Power Allocation for Cellular Systems with Data Rate-based Deep Neural Network, IEEE WCNC, 2022.

    6. Jia Guo and Chenyang Yang: Learning Power Control for Cellular Systems with Heterogeneous Graph Neural Network, IEEE WCNC, 2021.

    7. Jia Guo and Chenyang Yang: Structure of Deep Neural Networks with a Priori Information in Wireless Tasks, IEEE ICC, 2020.

    8. Jia Guo, Changyang She and Chenyang Yang: Predictive Resource Allocation with Coarse-grained Mobility Pattern and Traffic Load Information, IEEE ICC, 2018.

    9. Jia Guo and Chenyang Yang: Predictive Resource Allocation with Deep Learning, IEEE VTC Fall, 2018.

    10. Jia Guo, Chuting Yao and Chenyang Yang: Proactive Resource Allocation Planning with Three-levels of Context Information, IEEE/CIC ICCC, 2016.

    11. Baichuan Zhao, Jia Guo and Chenyang Yang: Learning Precoding Policy: CNN or GNN?, IEEE WCNC, 2022.

    12. Zhaoqi Xu, Jia Guo and Chenyang Yang: Predictive Resource Allocation with Interference Coordination by Deep Learning, IEEE WCSP, 2019.

    13. Chuting Yao, Jia Guo and Chenyang Yang: Achieving High Throughput with Predictive Resource Allocation, IEEE GlobalSIP, 2016.

    Patents

    1. 郭佳，杨晨阳，王坚，李榕，一种基于模型知识的低样本复杂度神经网络，国家发明专利，申请号202110530279.4。

    2. 郭佳，佘昌洋，杨晨阳，一种基于粗略信息的无线预测资源分配方法，国家发明专利，授权号：ZL201810481065.0。