Our flagship research prior to 2018 has been physical-layer network coding (PNC), a wireless-networking concept first put forth by the group in 2006.

Going forward, three research focuses of the group will be

1. Industrial wireless networks for IIoT communication (particularly specialized clean-slate network designs for the factory setting rather than Wi-Fi or 5G networks for general applications).

2. AI-driven wireless communications system and wireless networks.

3. Network protocol and network architecture designs for blockchain, with the aim to boost transaction throughput.

(see Publications and Research for more details).

Some representative publications by the group in these directions are as follows:

1. J. Liang, H. Chen, S. C. Liew, “Design and Implementation of Time-Sensitive Wireless IoT Networks on Software-Defined Radio,” IEEE Internet of Things Journal, early access, DOI: 10.1109/JIOT.2021.3094667, Jul. 2021.

2. L. Zhang, T. Wang, S. C. Liew, “Speeding up Block Propagation in Bitcoin Network: A Cut-through Relaying Scheme,” The 14th IEEE International Conference on Cyber, Physical and Social Computing (IEEE CPSCom-2021), Dec. 2021. (Won best student paper award)

3. Y. Yu, S. C. Liew, T. Wang, “Non-Uniform Time-Step Deep Q-Network for Carrier-Sense Multiple Access in Heterogeneous Wireless Networks,” IEEE Trans. on Mobile Computing, vol. 20, no. 9, pp. 2848-2861, DOI: 10.1109/TMC.2020.2990399, Sep. 2021.

4. T. Wang, C. Zhao, Q. Yang, S. Zhang, S. C. Liew, “Ethna: Analyzing the Underlying Peer-to-Peer Network of Ethereum Blockchain,” IEEE Trans. on Network Science and Engineering, vol. 8, no. 3, pp. 2131-2146, DOI: 10.1109/TNSE.2021.3078181, Jul.-Sep. 2021.

5. T. Wang, S. C. Liew, S. Zhang, “When Blockchain Meets AI: Optimal Mining Strategy Achieved by Machine Learning,” International Journal of Intelligent Systems, vol. 36, no. 5, pp. 2183-2207, DOI: 10.1002/int.22375, Feb. 2021.

6. Y. Shao, S. C. Liew, J. Liang, “Sporadic Ultra-Time-Critical Crowd Messaging in V2X,” IEEE Trans. on Commun., vol. 69, no. 2, pp. 817-830, DOI: 10.1109/TCOMM.2020/3037550, Feb. 2021.

7. Y. Shao, S. C. Liew, T. Wang, “AlphaSeq: Sequence Discovery with Deep Reinforcement Learning”, IEEE Trans. Neural Networks and Learning Systems, IEEE Trans. Neural Networks and Learning Systems, vol. 31, no. 9, pp. 3319-3333, DOI: 10.1109/TNNLS.2019.2942951, Sep. 2020. (This work adapts and modifies the AlphaGo algorithm to discover 0-1 sequences for communications and radar applications.)

8. Y. Shao, A. Rezaee, S. C. Liew, V. Chan, “Significant Sampling for Shortest Path Routing: A Deep Reinforcement Learning Solution,” IEEE JSAC Special Issue on Advances in Artificial Intelligence and Machine Learning for Networking, vol. 38, no. 10, pp. 2234-2248, DOI: 10.1109/JSAC.2020.300036, Oct. 2020. (A collaboration with MIT researchers to use the AI technique of deep reinforcement learning to solve problems in network routing.)

9. T. Wang, S. C. Liew, S. Zhang, “PubChain: A Decentralized Open-Access Publication Platform with Participants Incentivized by Blockchain Technology,” 2020 International Symposium on Networks, Computers and Communications (ISNCC 2020): Blockchains and Finance Technology, Oct 2020.

10. Y. Yu, T. Wang, S. C. Liew, “Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks,” IEEE J. on Selected Areas in Commun., vol. 37, no. 6, pp. 1277-1290, DOI: 10.1109/JSAC.2019.2904329, Jun. 2019. (This work makes use of deep-reinforcement learning techniques to design a MAC protocol that can co-exist harmoniously with other MAC protocols, without inner knowledge on how these other MAC protocols operate.)

(see Publications and Research for more details).