Yi Chen received her B.Eng. degree from the Department of Electronic and Information Engineering, Beijing University of Posts and Telecommunications in 2007, and her Ph.D. degree from the Department of Information Engineering, The Chinese University of Hong Kong in 2012. From 2012 to 2015, she worked as a postdoctoral researcher in the Department of Electronic Engineering, City University of Hong Kong. From 2015 to 2016, she worked as a research associate in the Department of Information Engineering, The Chinese University of Hong Kong. She joined The Chinese University of Hong Kong (Shenzhen) in 2016, where she is currently a Research Assistant Professor. She has also been working as a research scientist with Shenzhen Research Institute of Big Data since 2016.


Her research area focuses on communication networks and information systems. She has carried out several works in the fields of network optimization, information theory, control theory and machine learning. She has published more than thirty papers in international journals and conferences, such as IEEE Transactions on Information Theory, IEEE Transactions on Wireless Communications, IEEE Transaction on Communications. She, as the first author, received the Best Paper Award in IEEE/IET CSNDSP 2016.  She was selected to the Oversea High-level Talents Program of Shenzhen and Guangdong in 2016 and 2017, respectively.

She has led one NSFC youth project and one Shenzhen fundamental research project. She has been participating as a core member in several projects, including National Key R&D Program project and Pearl River Innovation and Entrepreneurship Team Project. She serves as Technical Program Committee for conferences IEEE ICC, PIMRC, WCNC.


Non-orthogonal Multiple Access Systems  

Non-orthogonal multiple access (NOMA) has attracted much attention as a promising technology for 6G and beyond wireless communication networks, due to its potential to enable large-scale device connectivity and improve system spectral efficiency. Broadly speaking, NOMA can be implemented in both the power and code domains. While code domain NOMA uses user-specific spreading sequences to share the entire resource, power domain NOMA exploits the channel gain differences between users and multiplexes them through power control. Moreover, in power domain NOMA, the receiver(s) applies successive interference cancellation (SIC) to decode the messages of different users in a sequential manner.

Both power control and SIC decoding order are key factors of NOMA. Most of existing NOMA designs have routinely relied on assuming a predefined SIC decoding order and discussed the power allocation only. The performance degradation due to fixed decoding orders is not yet well studied. The objective of our work is to jointly exploit the power allocation and the SIC decoding order in NOMA systems. In particular, we are interested in characterizing the achievable rate region of a system consisting of multiple NOMA sessions and in establishing a duality between the downlink and uplink NOMA systems.


Packet Routing with Reinforcement Learning

A communication network consists of a set of nodes and links that connect them. Information is transferred from one node to another node as data packets. Routing is a fundamental problem in communication networks that decides how the packets are directed from their source nodes to their destination nodes through some intermediate nodes. A packet on its way experiences two kinds of delay, one being the transmission delay over the links, and the other being the queue delay occurred when the packet has to wait in the queues of the nodes before being processed. Normally, there are multiple routing paths that a packet could take and the choice of the path is crucial to the delivery time of the packet. A successful routing scheme should be able to efficiently utilize the communication paths and minimize the delivery time of packets. 

With the increasing complexity of network topology and highly dynamic traffic demand, conventional model-based and rule-based routing schemes show significant limitations. For exmaple, the classic routing protocol, shortest path, always sends packets through paths that can minimize the number of hops. When the traffic load increases, the links shared by multiple shortest paths may suffer from terrible congestion.  Adding intelligence to the network control is becoming a trend and the key to achieving high-efficiency network operation. Our work is to develop a model-free and data-driven routing strategy by leveraging reinforcement learning.


Radio Resource Management for Wireless Networks

Wireless technologies are being used in various applications for their ease of deployment and inherent capabilities to support mobility. Wi-Fi, for example, is one of the fastest growing wireless technologies, with Wi-Fi chipsets and clients installed in almost every eletronic devices.  Persistent connectivity requires bandwidth and resources, and wireless spectrum is becoming even more precious than ever. Radio resource management (RRM) analyzes the existing RF environment, automatically adjusts each users' power and channel configurations to help mitigate such things as co-channel interference and signal coverage problems.

Link adaptation is a key function of RRM. In a fading wireless channel, link adaptation adapts the data transmission parameters, for example the modulation and coding scheme (MCS), to optimize the link performance in real time. Efficient and robust link adaptation is central to achieving the extremely high data rates supported by the wireless networks. Our work is to investigate an efficient rate adaptation mechanism, that can respond quickly to channel variation.



  1. 1. M. Yuan, Q. Cao, M.-O. Pun and Y. Chen, “Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning," accepted for publication in APSIPA Transations on Signal and Information Processing, 2022. 

  2. 2. Q. Cao, M.-O. Pun and Y. Chen, “Deep Learning in Network-Level Performance Prediction Using Cross-Layer Information," IEEE Transactions on Network Science and Engineering, vol. 9, no. 4, pp. 2364-2377, 1 July-Aug. 2022.

3. X. Zhang, Y. Chen*, X. Sun, G. Zhu, T. Q. Quek and Z. Zhang, “Uplink-Downlink Duality of Multi-Cell Non-orthogonal Multiple Access Systems,”  IEEE Transactions on Wireless Communications, vol. 21, no. 7, pp. 5035-5048, July 2022.

  1. 4. C. W. Sung, Y. Chen and Y. Gu, “Distributed Dual Optimization for the Uplink of Multi-Cell NOMA”, IEEE Transactions on Communications, vol. 69, no. 5, pp. 3135-3146, May 2021.

  2. 5. W. Chen, S. Zhao, R. Zhang, Y. Chen, L. Yang, “UAV-Assisted Data Collection with Non-Orthogonal Multiple Access,” IEEE Internet of Things Journal, vol. 8, no. 1, pp. 501-511, 1 Jan.1, 2021. 

  3. 6. Y. Zhang, Y. Chen, Y. Luo, and W. S. Wong, “Achieving Zero-packet-loss Throughput 1 for a Collision Channel Without Feedback and with Arbitrary Time Offsets,” IEEE Transactions on Information Theory, vol. 66. no. 4, April 2020.

7. Y. Chen* and C. W. Sung, “Characterization of SINR Region for Multiple Interfering Multicast in Power-controlled Systems,” IEEE Transactions on Communication, vol. 67, no. 1, pp. 165-175, Jan. 2019.

8. Y. Chen*, Y.-H. Lo, K. W. Shum, W. S. Wong, and Y. Zhang, “CRT Sequences with Applications to Collision Channels Allowing Successive Interference Cancellation,” IEEE Transactions on Information Theory, vol. 64, no. 4, pp. 2910 - 2923, April 2018.

9. Y. Chen*C. W. Sung, S.-W. Ho, and W. S. Wong, “BER Analysis and Power Control for Interfering Visible Light Communication Systems,” Optik-International Journal for Light and Electron Optics, vol. 151, pp. 98-109, Dec. 2017.

10. Y. Fu, Y. Chen, and C. W. Sung, “Distributed Power Control for the Downlink of Multi-cell NOMA systems,” IEEE Transactions on Wireless Communications, vol. 16, no. 9, pp. 6207-6220, Sept. 2017.

11. Y.-H. Lo, Y. Zhang, Y. Chen, H.-L. Fu, and W. S. Wong, “The Global Packing Number of a Fat-tree Network,” IEEE Transactions on Information Theory, vol.63, no. 8,  pp. 5327-5335, Aug. 2017.

12. Y. Chen*, S. Yang, and W. S. Wong, “Exact non-Gaussian Interference Model for Fading Channels,” IEEE Transactions on Wireless Communications, vol. 12, no. 1, pp. 168-179, Jan. 2013.

13. Y. Chen* and W. S. Wong, “Power Control for Non-Gaussian Interference,” IEEE Transactions on Wireless Communications, vol. 10, no. 8, pp. 2660-2669, Aug. 2011.


1. X. Mai, Q. Fu and Y. Chen*, “Packet Routing with Graph Attention Multi-agent Reinforcement Learning,” in Proceedings of IEEE Global Communications Conference (GLOBECOM), Dec. 2021.

2. M. Yuan, Q. Cao, M. Pun and Y. Chen, “Multi-Agent Reinforcement Learning-Based Fairness-Aware Scheduling for Bursty Traffic,” in Proceedings of IEEE Global Communications Conference (GLOBECOM), Dec. 2021.

3. W. Chen, S. Zhao, R. Zhang, Y. Chen, and L. Yang, “Generalized User Grouping in NOMA Based on Overlapping Coalition Formation Game,” in Proceedings of IEEE Global Communications Conference (GLOBECOM), Dec. 2020.

4. W. Chen, S. Zhao, R. Zhang, Y. Chen, and L. Yang, “Machine Learning-Based Generalized User Grouping in NOMA,” in Proceedings of IEEE Global Communications Conference (GLOBECOM), Dec. 2020.

5. S. Zeng, X. Xu, and Y. Chen*, “Multi-Agent Reinforcement Learning for Adaptive Routing: A Hybrid Method using Eligibility Traces,” The 16th IEEE International Conference on Control and Automation (ICCA), October, 2020.

6. X. Ding, Y. Chen*, and Y. Lu, “Joint User Ordering, Beamforming and Power Allocation for Downlink MIMO-NOMA Systems,” 12th IEEE/IETInternational Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), August, 2020.

7. Q. Cao, S. Zeng, W. Pan, and Y. Chen, “Network-Level System Performance Prediction Using Deep Neural Networks with Cross-Layer Information,” in Proceedings of IEEE International Conference on Communications (ICC), June, 2020.

8. C. Gu, Y. Chen*, W. Li, and C. Liu, “To Shift Tasks or To Shift Energy by ESDs? An Economical Scheduling for Cloud Data Center,” in Proceedings of 2019 IEEE International Symposium on Parallel and Distributed Processing and Applications (ISPA), Xiamen, China, September 2019. 

9. L. Han, C. Gu, Y. Chen, and W. Li, “An efficient, secure and reliable search scheme for dynamic updates with blockchain,” in Proceedings of 2019 International Conference on Communication and Network

  Security (ICCNS), November, 2019.

10. Y. Chen*, K. W. Shum, and W. S. Wong “CRT Sequences for Medium Access Control,” in Proceedings of IEEE/CIC International Conference on Communications in China (ICCC), Changchun, Aug., 2019.

11. X. Zhang, Y. Chen*, J. Zhang, Y. Lei, C. Shen, and G. Zhu, “Characterization of SINR region for multi-cell downlink NOMA systems,” in Proceedings of IEEE International Conference on Communications (ICC), Shanghai, May, 2019.

12. J. Luo, Y. Chen, and W. S. Wong, “Utilizing In-Network Buffering for Scheduling and Routing in Data Center Networks,” International Symposium on Mobile Ad Hoc Networking and Computing (MOBIHOC), Jun. 2018. 

13. Q. Guo, C. W. Sung, Y. Chen, and C. S. Chen, “ Power Control for Coordinated NOMA Downlink with Cell-Edge Users,” in Proceedings of IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain, 2018.

14. Kenneth W. Shum, Y. Chen, Y.-H. Lo, W. S. Wong, and Y. Zhang, “Protocol-sequence-based Media-access control with Successive Interference Cancellation,” in Proceedings of The 10th International Conference on Wireless Communications and Signal Processing (WCSP), Hangzhou, China, Oct. 2017.

15. Y. Zhang, Y. Chen, Y.-H. Lo, and W. S. Wong, “ The Zero-error Capacity of a Collision Channel with Successive Interference Cancellation,” in Proceedings of  IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, June 2017.

16. Lou Salaun, C. S. Chen, Y. Chen, and W. S. Wong, “Constant Delivery Delay Protocol Sequences for the Collision Channel without Feedback,” in Proceedings of The 19th International Symposium on Wireless Personal Multimedia Communications (WPMC), Shenzhen, China, Nov. 2016.

17. Y. Chen*, Kenneth W. Shum, and W. S. Wong, “Generalized CRT Sequence and its Applications,” in Proceedings of Sequence and Their Applications (SETA), Chengdu, China, Oct. 2016.

18. F. Liu, Y. Chen, and W. S. Wong, “An Asynchronous Load Balancing Scheme for Multi-server Systems,” in Proceedings of Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, USA, Oct. 2016.

19. Y. Chen*, C. W. Sung, S.-W. Ho, and W. S. Wong, “BER Analysis for Interfering Visible Light Communication Systems,” in Proceedings of IEEE/IET International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), Prague, Czech Republic, Jul. 2016. (Best Paper Award)

20. Y. Fu, Y. Chen, and C. W. Sung, “Distributed Downlink Power Control for the Non-orthogonal Multiple Access System with Two Interfering Cells,” in Proceedings of IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia, May, 2016.

21. S. Yang, Y. Chen, S. C. Liew, and L. You, “Coding for Network-coded Slotted ALOHA,” in Proceedings of IEEE Information Theory Workshop (ITW), Jerusalem, Israel, April 2015.

22. Y. Chen* and C. W. Sung, “Resource Allocation for Two-way Relay Cellular Networks With and Without Network Coding,” in Proceedings of IEEE International Conference on Communication Systems (ICCS), Macau, China, Nov. 2014.

23. S. Yang, S. C. Liew, L. You, and Y. Chen, “Linearly-coupled Fountain Codes for Network-coded Multiple Access,” in Proceedings of IEEE Information Theory Workshop (ITW), Hobart, Tasmania, Australia, Nov. 2014.

24. H. Cheng, Y. Chen, W. S. Wong, Q. Yang, and L. Shen, “Protocol Sequence Based Wireless Media Access Control in Networked Control Systems,” in Proceedings of IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV), Guangzhou, China, Dec, 2012.

25. Y. Chen* and W. S. Wong, “Power Control for Non-Gaussian Interference,” in Proceedings of IEEE International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT), Seoul, Korea, 2009.