Dr CHAN, Tse Tin David   陳謝天
Assistant Professor
Department of Mathematics and Information Technology
Phone No: (852) 2948 7864
Email: tsetinchan@eduhk.hk
Contact
ORCiD
0000-0001-9235-7813
Phone
(852) 2948 7864
Email
tsetinchan@eduhk.hk
Address
10 Lo Ping Road, Tai Po, New Territories, Hong Kong
Website
https://pappl.eduhk.hk/rich/web/person.xhtml?pid=291650&name=CHAN, Tse Tin David
Scopus ID
57190584574
Research Interest
  • Wireless Communications and Networking
  • Internet of Things (IoT)
  • Age of Information (AoI)
  • Machine Learning for Communications
Teaching Interest
  • Wireless Communications
  • Computer Network
  • Machine Learning
  • Deep Learning
External Appointment

Technical Review

  • IEEE Journal on Selected Areas in Communications
  • IEEE Transactions on Wireless Communications
  • IEEE Transactions on Communications
  • IEEE Transactions on Signal Processing
  • IEEE Transactions on Vehicular Technology
  • IEEE Internet of Things Journal
  • IEEE Wireless Communications Letters
  • IEEE ICC 2016, 2017, 2019, 2020
  • IEEE GLOBECOM 2016, 2017, 2018, 2019, 2020
  • IEEE ISIT 2017

etc.

Personal Profile

We are looking for PhD students and Postdocs to join our group. My group is currently focused on wireless communications for the Industrial Internet of Things (IIoT) and the application of AI technologies in the PHY and MAC layers of wireless networks. If you are interested, please send me your CV and transcripts.

For more details about my profile, please visit my personal website: https://ttchan.net/

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Tse-Tin Chan (Member, IEEE) received his B.Eng. (First Class Honours) and Ph.D. in Information Engineering from the Chinese University of Hong Kong (CUHK) in 2014 and 2020, respectively. He is currently an Assistant Professor in the Department of Mathematics and Information Technology at the Education University of Hong Kong (EdUHK). Before that, he was an Assistant Professor in the Department of Computing at the Hang Seng University of Hong Kong (HSUHK) from 2020 to 2022. His research interests include wireless communications and networking, Internet of Things (IoT), age of information (AoI), and machine learning for communications.

Research Interest
  • Wireless Communications and Networking
  • Internet of Things (IoT)
  • Age of Information (AoI)
  • Machine Learning for Communications
Teaching Interest
  • Wireless Communications
  • Computer Network
  • Machine Learning
  • Deep Learning
External Appointment

Technical Review

  • IEEE Journal on Selected Areas in Communications
  • IEEE Transactions on Wireless Communications
  • IEEE Transactions on Communications
  • IEEE Transactions on Signal Processing
  • IEEE Transactions on Vehicular Technology
  • IEEE Internet of Things Journal
  • IEEE Wireless Communications Letters
  • IEEE ICC 2016, 2017, 2019, 2020
  • IEEE GLOBECOM 2016, 2017, 2018, 2019, 2020
  • IEEE ISIT 2017

etc.

Selected Output
Journal Publications
H. Pan, Y. Zhou, T.-T. Chan, M. Tang, J. Li, and Z. Du, “Improving information freshness via backbone-assisted cooperative access points,” IEEE Internet of Things Journal, vol. 10, no. 5, pp. 4592-4604, Mar. 2023.
H. Pan, T.-T. Chan, J. Li, and V. C. M. Leung, “Age of information with collision-resolution random access,” IEEE Transactions on Vehicular Technology, vol. 71, no. 10, pp. 11295-11300, Oct. 2022.
Q. Ren, T.-T. Chan, H. Pan, K.-H. Ho, and Z. Du, “Information freshness and energy harvesting tradeoff in network-coded broadcasting,” IEEE Wireless Communications Letters, vol. 11, no. 10, pp. 2061-2065, Oct. 2022.
H. Pan, T.-T. Chan, V. C. M. Leung, and J. Li, “Age of information in physical-layer network coding enabled two-way relay networks,” IEEE Transactions on Mobile Computing, early access. doi: 10.1109/TMC.2022.3166155.
T.-T. Chan, H. Pan, and J. Liang, “Age of information with joint packet coding in industrial IoT,” IEEE Wireless Communications Letters, vol. 10, no. 11, pp. 2499-2503, Nov. 2021.
T.-T. Chan and T.-M. Lok, “Utilizing interference by network coding for simultaneous wireless information and power transfer,” IEEE Wireless Communications Letters, vol. 10, no. 6, pp. 1349-1353, Jun. 2021.
T.-T. Chan and T.-M. Lok, “Signal-aligned network coding for multicell processing with limited cooperation,” IEEE Transactions on Communications, vol. 68, no. 8, pp. 4832-4843, Aug. 2020.

Conference Papers
S. Yang, H. Pan, T.-T. Chan, and Z. Wang, “Semantic communication-empowered physical-layer network coding,” in Proc. IEEE Wireless Communications and Networking Conference (IEEE WCNC), Mar. 2023, pp. 1-6.
Z. Zou, T.-T. Chan, H. Pan, and T.-M. Lok, “Age of information and energy harvesting tradeoff for joint packet coding in downlink IoT networks,” in Proc. IEEE Vehicular Technology Conference (IEEE VTC), Jun. 2022, pp. 1-5.
J. Feng, H. Pan, T.-T. Chan, and J. Liang, “Timely status update: Should ARQ be used in two-hop networks?,” in Proc. IEEE International Conference on Communications (IEEE ICC), May 2022, pp. 3514-3519.
Q. Ren, T.-T. Chan, J. Liang, and H. Pan, “Age of information in SIC-based non-orthogonal multiple access,” in Proc. IEEE Wireless Communications and Networking Conference (IEEE WCNC), Apr. 2022, pp. 800-805.
T.-T. Chan and T.-M. Lok, “Interference alignment with physical-layer network coding in MIMO relay channels,” in Proc. IEEE International Conference on Communications (IEEE ICC), May 2016, pp. 1–6.

Project
Ultra-reliable low-latency communications for vehicle-to-everything: From theory to practical implementation
This project is supported by the Research Matching Grant Scheme of the Research Grants Council of Hong Kong and in-kind donations from industrial partners with a total amount of HKD 15,546,000. (HKD 7,773,000 from the Research Grants Council of Hong Kong and HKD 7,773,000 in-kind donations from industrial partners.)

The project aims to develop and validate ultra-reliable low-latency communications (URLLC) schemes for Vehicle-to-Everything (V2X). To start with, we would like to propose new URLLC performance metrics to meet the needs of V2X. Based on the proposed performance metrics, we will develop and optimize wireless communication schemes and networking protocols. Finally, we will utilize the donated packages to build prototypes for validating our proposed schemes.
Project Start Year: 2023, Principal Investigator(s): CHAN, Tse Tin David 陳謝天
 
Enhancing wireless information freshness via physical-layer network coding and non-orthogonal multiple access
(Note: Dr. Tse-Tin Chan was the Principal Investigator of this project. After Dr. Chan moved from HSUHK to EdUHK on August 1, 2022, the Principal Investigator was transferred to Dr. Hai Liu to meet the funding eligibility. Currently, Dr. Chan is the Co-Investigator of this project.)

This project is supported by the Faculty Development Scheme of the Research Grants Council of Hong Kong under Grant UGC/FDS14/E02/21 in the amount of HKD 1,143,229.

The Internet of Things (IoT) is an emerging wireless communication and networking technology that can be utilized to connect billions of devices and establish a close connection between our physical world and computer networks. Many time-critical applications, such as autonomous vehicles and industrial control, require the support of ultra-reliable low-latency communications (URLLC) to convey fresh information updates. However, information freshness cannot be accurately quantified by traditional metrics such as throughput and delay. Therefore, the age of information (AoI) metric has recently received extensive attention from researchers. AoI is defined as the elapsed time since the most recently received packet was generated. Literature shows that replacing traditional performance metrics with AoI may lead to fundamental changes in the communication system designs.

Most AoI research has focused on the upper layers of communication networks. Lower-layer solutions, such as multiple access schemes for the medium access control (MAC) layer and multi-user interference cancellation schemes for the physical (PHY) layer, have not been thoroughly studied for their impact on information freshness. Existing lower-layer designs cannot guarantee good information freshness when a large number of users access complicated and unreliable wireless channels. This problem seriously hinders the development of time-critical IoT applications. Moreover, information update packets in the IoT networks are usually very short. Shannon’s channel capacity formula in information theory assumes an infinite blocklength and is therefore not suitable for characterizing the performance of short-packet communications.

The purpose of this project is to fill the above-mentioned research gaps. To begin with, we would like to develop a theoretical framework for AoI analyses in various error-prone short-packet wireless communication models. Based on the developed framework, we then design lower-layer algorithms to enhance information freshness by physical-layer network coding (PNC) and non-orthogonal multiple access (NOMA). PNC alleviates the multi-user interference problem by utilizing the network-coded packets decoded from superimposed signals. NOMA improves spectral efficiency by serving multiple users at the same time and frequency. Our preliminary simulations show that PNC and NOMA can significantly improve the AoI performance of many channel models. To the end, we would investigate the combination of PNC and NOMA to improve the AoI performance further. If this research achieves favorable outcomes, it will be a solid step in the theory and practice of enhancing information freshness in the next-generation IoT networks.
Project Start Year: 2022, Principal Investigator(s): CHAN, Tse Tin David 陳謝天, Hai Liu
 
Intelligent wireless edge networks: Theories and applications
(Note: Dr. Tse-Tin Chan was the Principal Investigator of this project. After Dr. Chan moved from HSUHK to EdUHK on August 1, 2022, the Principal Investigator was transferred to Dr. Kin-Hon Ho to meet the funding eligibility. Currently, Dr. Chan is the Co-Investigator of this project.)

This project is supported by the Research Matching Grant Scheme of the Research Grants Council of Hong Kong and in-kind donations from industrial partners with a total amount of HKD 13,338,000. (HKD 6,669,000 from the Research Grants Council of Hong Kong and HKD 6,669,000 in-kind donations from industrial partners.)

With the rapid growth of smart Internet of Things (IoT) devices, intelligent wireless edge networks have been receiving significant attention. Intelligent wireless edge networks enable distributed edge learning to support artificial intelligence (AI) applications on various end devices. Distributed edge learning overcomes the limited computing power, energy resources, and data on end devices. It also significantly reduces latency compared to conventional machine learning approaches that require training data and inference processes to be centralized in a data center or on a cloud. With the advanced development of wireless networks, we expect more powerful network intelligence to emerge in the future. However, there are many challenges with intelligent wireless edge networks. In this project, we focus on the following objectives. (1) To establish theoretical frameworks for low-latency wireless edge networks; (2) To develop new communication schemes and networking solutions for the intelligent Internet of Things (IoT); (3) To build prototypes and applications for wireless edge intelligence. If the research project yields favorable outcomes, it will be a great advance in the theory and practice of supporting next-generation intelligent wireless edge networks.
Project Start Year: 2022, Principal Investigator(s): CHAN, Tse Tin David 陳謝天, Kin-Hon Ho