Advanced Network Technology (ANT)
Research Field
Jen-yi Pan
received the B.S. and Ph.D. degrees in computer science from National Tsing-Hua University, Hsinchu, Taiwan, R.O.C., in 1995 and 2002, respectively. He is currently an associate professor with the Department of Communcations Engineering, National Chung Cheng University, Chia-Yi, Taiwan, R.O.C. His research interests include performance evaluation of medium access control, non-terrestrial networks, and system-level simulation. Dr. Pan is a member of ACM, IEEE, and IEICE.
He has been implementing industry-university cooperation projects with ITRI (Industrial Technology Research Institute, Taiwan) for many years, including map-based channel implementation, flexible time division duplexing, evaluating IMT-2020 system performance, and developing 3GPP NewRadio R-16 URLLC functionality.
He has also served on the technical committees of several international conferences, including APNOMS, GLOBECOM, IEEE 5G World Forum, and APSIPA ASC.
We extend a warm welcome to students from all corners of the world.
ANT (Advanced Network Technology) Laboratory was founded in March 2003, under the guidance of Dr. Jen-Yi Pan. Dr. Pan is dedicated to the principle of inclusive education and wholeheartedly invites talented individuals from around the globe to join us on this journey.
Should you have any questions, please inquire
jypan at comm dot ccu dot edu dot tw.
- B5G, 6G system level simulator
- AI-based wireless communication
The best paper award in the 8th IEEE Asia Pacific Conference on Wireless and Mobile, October 2023
PhD in Computer Science, National Tsing Hua University, Taiwan
Bachelor of Computer Science, National Tsing Hua University, Taiwan
1 Vacancy
Job Description
RIS-assisted system framework by addressing critical challenges in secure wireless communication. The inclusion of active RIS, MIMO, and eavesdropper scenarios marks a significant advancement, making it well-suited for future 6G networks. This proposal extends the framework outlined in the attached paper, aiming to incorporate novel elements and methodologies to address secrecy and reliability in RIS-assisted communication systems. Our focus is on maximizing the secrecy sum rate or minimizing outage probability while leveraging active RIS elements, MIMO configurations, and direct links between the base station and users.
This project introduces an eavesdropper into the system model to analyze and enhance secrecy performance by optimizing system parameters to maximize the secrecy sum rate and ensure robust secure communication. Active RIS elements will be employed to amplify signals, offering greater control and improved reliability and security compared to passive RIS. The system transitions from MISO to MIMO architecture, enhancing spatial diversity, multiplexing gains, and channel capacity for robust communication in challenging scenarios. Direct base station-user links are incorporated to provide an additional transmission path, improving system reliability in the presence of eavesdroppers or RIS failures. Frequency-selective fading channels are included to make the model more realistic for practical wireless environments with multipath propagation. Rigorous mathematical formulations will model the proposed system and derive performance metrics, complemented by numerical simulations to validate the effectiveness of the enhancements.
Methodology: Develop optimization frameworks for secrecy rate maximization and outage probability minimization. Utilize active RIS to dynamically control phase shifts and power levels. Design advanced MIMO precoding techniques to optimize transmission power allocation across multiple antennas. Implement deep learning-based methods to handle high-dimensional optimization problems, extending the DRL approach used in the original paper.
Expected Contributions: Enhanced secrecy and reliability for RIS-assisted communication systems. Feasibility demonstration of active RIS in practical MIMO systems. Analytical insights and simulation results for secure communication in frequency-selective fading environments.
Preferred Intern Education Level
Excellent Master's and PhD students.
Skill sets or Qualities
1) Wireless Communication, Reconfigurable Intelligent Surface (RIS), Satellite Communication
2) Extra Knowledge of Deep Learning and Machine Learning will be preferred
1 Vacancy
Job Description
Developing RISs. Bring to light the third generation of meta-materials technology by developing RISs that can be reconfigured in real-time and are able to perform joint communication and sensing tasks.
Preferred Intern Education Level
Excellent Master's and PhD students.
Skill sets or Qualities
1) Wireless Communication, Reconfigurable Intelligent Surface (RIS), Satellite Communication
2) Extra Knowledge of Deep Learning and Machine Learning will be preferred