CV

Education

• Ph.D in Information and Communication Engineering, South China University of Technology, 2028 (expected)
• M.Eng. in Information and Communication Engineering, Beijing Jiaotong University, 2024
• B.Eng. in Communication Engineering, GitHub Beijing Jiaotong University, 2021

Academic Project experience

• 2023-2024: Deep Learning based Prediction Model Design for Predicting Rain Fade Signal Attenuation for Microwave and mmWave Bands Master Thesis Disertation
  • Beijing Jiaotong University
  • Duties includes:
    • Study existing rain attenuation prediction models(including ITU-R models, Global Crane Model and other research works)
    • Data Collection from the target geographical location(Addis Ababa, Ethiopia), Data includes Rain Rate and Microwave/mmWave link profile data
    • Design deep learning prediction model(LSTM) which uses rain rate and microwave/mmWave link profile data as input and predict the attenuation that will be caused on the target communication link.
    • Preprocess and analyse the collected data based on the design requirments
    • Train and simulate the design using the preprocessed data
    • Summarize the Conclusion and write dissertation
    • Participated in the 4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE), Zhuhai, China and my work has been accepted for publication in this confference.
      • 4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE), Zhuhai, China
      • Article Title: Rain Attenuation Prediction Modeling for Microwave and Millimeter Wave Band Using LSTM
  • Supervisor: Prof. Xiong Lei
• 2022-2023: Study of Characteristics Analysis and Modeling of Train-to-Train Wireless Channel in Station Scenario, group work under Prof. Xiong Lei at The State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing 100044, China
  • Beijing Jiaotong University
  • Duties included:
    1. Literature Review:
       • Review existing research on Train-to-Train (T2T) wireless communication channels.
       • Investigate previous studies on ray tracing (RT) techniques in wireless communications, particularly for high-speed rail scenarios.
    2. Selection of Frequency Band:
       • Choose the 2.1 GHz frequency band for analysis, considering its relevance for T2T communication.
    3. Ray Tracing (RT) Simulation Setup:
       • Develop or use existing RT tools to simulate the wireless channel characteristics.
       • Set up the simulation environment, including models of high-speed trains and surrounding environments.
    4. Parameter Identification:
       • Identify key channel characteristics to analyze: path loss, multipath clustering, delay spread, Rice factor, angular spread, and channel non-stationarity.
    5. Simulation Execution:
       • Run simulations to model the T2T wireless channel under different environmental conditions.
       • Analyze how changes in the environment impact channel characteristics.
    6. Data Collection and Analysis:
       • Collect data from the simulations, focusing on path loss, multipath clustering, and other identified parameters.
       • Use statistical methods to analyze the distribution of path loss, delay spread, Rice factor, and angular spread.
    7. Non-Stationarity Analysis:
       • Introduce and calculate the time correlation coefficient (TPCC) of the power delay profile (PDP) to assess channel non-stationarity.
       • Determine the quasi-stationarity distance based on TPCC analysis.
    8. Model Validation:
       • Compare the simulation results with existing measured data to validate the accuracy of the RT model.
       • Quantify the consistency between the simulation results and real-world measurements.
    9. Channel Model Development:
       • Develop a channel model for the station scenario based on the simulation results.
       • Summarize the channel model parameters, such as path loss factors, shadow fading distribution, and multipath clustering characteristics.
    10. Scenario-Specific Analysis:
        • Study the impact of different distances between trains on channel characteristics.
        • Analyze how the proximity of trains affects channel correlation, multipath components (MPCs), and overall channel behavior.
    11. Performance Evaluation:
        • Evaluate the communication system performance in high-speed rail scenarios.
        • Determine the required frequency of channel estimation to maintain reliable communication.
    12. Discussion of Results:
        • Discuss the implications of the findings on T2T communication system design.
        • Highlight the need to consider rapid channel changes and estimation frequency in system design.
  • Supervisor: Prof. Xiong Lei

Publications

Rain Attenuation Prediction Modeling for Microwave and Millimeter Wave Band Using LSTM Permalink

Characteristics Analysis and Modeling of Train-to-Train Wireless Channel in Station Scenario Permalink

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