Research

Research topics include wireless communications and networking, network intelligence, URLLC, big data processing, IoT, and 6G.

AI

Network Intelligence
Recent breakthroughs in artificial intelligence and machine learning, including deep neural networks, the availability of powerful computing platforms and big data are providing us with technologies to perform tasks that once seemed impossible. At the heart of this technological revolution, it is clear that we will need network intelligence over a massively scalable, ultra-high capacity, ultra-low latency, and dynamic network infrastructure in the future. Some recent contributions include:

  • H. Lee, S. H. Lee, and T. Q. S. Quek, “Learning Autonomy in Management of Wireless Random Networks, ” IEEE Trans. Wireless Commun., 2021.
  • H. S. Jang, H. Lee, T. Q. S. Quek, and H. Shin, “Deep Learning based Cellular Random Access Framework,” IEEE Trans. Wireless Commun., 2021.
  • A. Liu, R. Yang, T. Q. S. Quek, and M.-J. Zhao, “Two-Stage Stochastic Optimization via Primal-Dual Decomposition and Deep Unrolling,” IEEE Trans. Signal Processing, vol. 69, pp. 3000-3015, May 2021.
  • Y.-C. Wu, Q. D. Thinh, Y. Fu, C. Lin, and T. Q. S. Quek, “A Hybrid DQN and Optimization Approach for Strategy and Resource Allocation in MEC Networks,” IEEE Trans. Wireless Commun., vol. 20, no. 7, pp. 4282-4295, Jul. 2021.
  • W. Xia, T. Q. S. Quek, K. Guo, W. Wen, H. Yang, and H. Zhu, “Multi-Armed Bandit Based Client Scheduling for Federated Learning,” IEEE Trans. Wireless Commun., vol. 19, no. 11, pp. 7108-7123, Nov. 2020.
  • K. Wei, J. Li, M. Ding, C. Ma, H. Yang, F. Farokhi, S. Jin, T. Q. S. Quek, and H. V. Poor, “Federated Learning with Differential Privacy: Algorithms and Performance Analysis,” IEEE Trans. Information Forensics, and Security, vol. 15, no. 4, pp. 3454-3466, Apr. 2020.
  • H. Yang, Z. Liu, T. Q. S. Quek, and H. V. Poor, “Scheduling Policies for Federated Learning in Wireless Networks,” IEEE Trans. Commun., vol. 68, no. 1, pp. 317-333, Jan. 2020.
  • H. Lee, S. H. Lee, and T. Q. S. Quek, “Deep Learning for Distributed Optimization: Applications to Wireless Resource Management,” IEEE J. Select. Areas Commun., vol. 37, no. 10, pp. 2251-2266, Oct. 2019.
  • Z, Liu, W. Zhang, S. Lin, and T. Q. S. Quek, “Heterogeneous Sensor Data Fusion by Deep Multimodal Encoding,” IEEE J. Select. Topics Signal Processing, vol. 11, no. 3, pp. 479-491, Apr. 2017.
fog

Edge/Fog Computing
As mobile applications are getting more complex and computationally intensive, computational tasks can be migrated to cloud servers. However, there is a significant problem of communication latency due to the long distance between mobile devices and cloud servers. To overcome these limitations, there is a strong interest to push computation, storage, communication, and intelligence to the edge, which is closer to users. Some recent contributions include:

  • K. Guo and T. Q. S. Quek, “On the Asynchrony of Computation Offloading in Multi-User MEC Systems,” IEEE Trans. Commun., 2020.
  • M. Siew, D. Cai, L. Li, and T. Q. S. Quek, “Dynamic Pricing for Resource-Quota Sharing in Multi- Access Edge Computing,” IEEE Trans. Network Science and Eng., 2020.
  • H. Hu, H. Shan, C. Wang, T. Sun, X. Zhen, L. Yu, Z. Zhang, and T. Q. S. Quek, “Video Surveillance on Mobile Edge Networks – A Reinforcement Learning Based Approach,” IEEE Internet of Things Journal, vol. 7, no. 6, pp. 4746–4760, Jun. 2020.
  • L. Li, T. Q. S. Quek, J. Ren, H. Y. Yang, Z. Chen, and Y. Zhang, “An Incentive-Aware Job Offloading Control Framework for Mobile Edge Computing,” IEEE Trans. Mobile Computing, 2020.
  • W. Xia, T. Q. S. Quek, J. Zhang, S. Jin, and H. Zhu, “Programmable Hierarchical C-RAN: From Task Scheduling to Resource Allocation,” IEEE Trans. Wireless Commun., vol. 18, no. 3, pp. 2003–2016, Mar. 2019.
  • M. Li, T. Q. S. Quek, and C. Courcoubetis, “Mobile Data Offloading with Uniform Pricing and Overlaps,” IEEE Trans. Mobile Computing, vol. 18, no. 2, pp. 348–361, Feb. 2019.
  • R.-H. Hsu, J. Lee, T. Q. S. Quek, and J.-C. Chen, “Reconfigurable Security: Edge Computing-based Framework for IoT,” IEEE Network Mag., vol. 32, no. 5, pp. 92–99, Oct. 2018.
  • T. Q. Dinh, J. Tang, Q. D. La, and T. Q. S. Quek, “Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling,” IEEE Trans. Commun., vol. 65, no. 8, pp. 3571-3584, Aug. 2017.

Unknown

Ultra-Reliable Low Latency Communications (URLLC)
URLLC will support advanced ultra-high reliable and low-latency services such as factory automation, autonomous driving, and industrial internet. This is a complete game changer for communications technology in the future and involves many fundamental questions that need to address from PHY, MAC, to network design. Some recent contributions include:

 

  • P. Yang, X. Xi, T. Q. S. Quek, J. Chen, X. Cao, and D. Wu, “RAN Slicing for Massive IoT and Bursty URLLC Service Multiplexing: Analysis and Optimization,” IEEE Trans. Wireless Commun., submitted.
  • P. Yang, X. Xi, T. Q. S. Quek, J. Chen, X. Cao, and D. Wu, “How Should I Orchestrate Resources of My Slices for Bursty URLLC Service Provision?,” IEEE Trans. Commun., submitted.
  • P. Yang, X. Xi, Y. Fu, T. Q. S. Quek, X. Cao, and D. Wu, “Multicast eMBB and Bursty URLLC Service Multiplexing in a CoMP-Enabled RAN,” IEEE Trans. Wireless Commun., revised.
  • C. She, Y. Duan, G. Zhao, T. Q. S. Quek, Y. Li, and B. Vucetic, “Cross-Layer Design for Mission Critical IoT in Mobile Edge Computing Systems,” IEEE IoT Journal, vol. 6, no. 6, pp. 9360-9374, Dec. 2019.
  • D. Feng, C. She, K. Ying, L. Lai, Z. Hou, T. Q. S. Quek, Y. Li, and B. Vucetic, “Towards Ultra-Reliable Low-Latency Communications: Typical Scenarios, Possible Solutions, and Open Issues,” IEEE Vehicular Technology Mag., vol. 14, no. 2, pp. 94-102, Jun. 2019.
  • Z. Hou, C. She, Y. Li, T. Q. S. Quek, and B. Vucetic, “Burstiness Aware Bandwidth Reservation for Ultra-reliable and Low-latency Communications (URLLC) in Tactile Internet,” IEEE J. Select. Areas Commun., vol. 36, no. 11, pp. 2401-2410, Nov. 2018. 
  • C. She, C. Yang, and T. Q. S. Quek, “Cross-layer Optimization for Ultra-Reliable and Low-Latency Radio Access Networks,” IEEE Trans. Wireless Commun., vol. 17, no. 1, pp. 127–141, Jan. 2018.
  • C. She, C. Yang, and T. Q. S. Quek, “Radio Resource Allocation for Ultra-Reliable and Low-Latency Communications,” IEEE Commun. Mag., vol. 55, no. 6, pp. 72-78, Jun. 2017.

vzb72f96c6386b4a1884b82437a877b877

Internet-of-Things (IoT)
The Internet of Things (IoT) will connect billions of devices, i.e. the things of our everyday life. This will open up new ways to monitor, assist, secure, control e.g. in the telemedicine area, smart homes, smart factory etc. In fact, IoT would likely change the way we see the Internet as a human-to-human interface towards a more general machine-to-machine platform, and subsequently machine-to-data platform. Some recent contributions include:

 

  • J. Yuan, Q. He, M. Matthaiou, T. Q. S. Quek, and S. Jin, “Towards Massive Connectivity for IoT in Mixed-ADC Distributed Massive MIMO,” IEEE Internet of Things Journal, vol. 19, no. 3, pp. 1704– 1721, Mar. 2020.
  • H. Wu, Z. Zhang, C. Jiao, C. Li, and T. Q. S. Quek, “Learn to Sense: a Meta-learning Based Sensing and Fusion Framework for Wireless Sensor Networks,” IEEE Internet of Things Journal, 2020.
  • H. S. Jang, H. Jin, B. C. Jung, and T. Q. S. Quek, “Versatile Access Control for Massive IoT: Throughput, Latency, and Energy Efficiency,” IEEE Trans. Mobile Computing, vol. 19, no. 8, pp. 1984–1997, Aug. 2020.
  • M. Ngo, Q. D. La, T. Q. S. Quek, and H. Shin, “User Behavior Driven MAC Scheduling for Body Sensor Networks: A Cross-layer Approach,” IEEE Sensors Journal, vol. 19, no. 17, pp. 7755-7765, Sep. 2019.
  • Z. Chen, Q. Yao, H. Yang, and T. Q. S. Quek, “Massive Wireless Random Access with Successive Decoding – Delay Analysis and Optimization,” IEEE Trans. Wireless Commun., vol. 67, no. 1, pp. 457-471, Jan. 2019.
  • H. Yang, Y. Wang, and T. Q. S. Quek, “Delay Analysis of Random Scheduling and Round Robin in Small Cell Networks,” IEEE Wireless Commun. Letters, vol. 7, no. 6, pp. 978–981, Dec. 2018.
  • P. Gope, J. Lee, and T. Q. S. Quek, “Light weight and Practical Anonymous Authentication Protocol for RFID Systems using Physically Unclonable Functions,” IEEE Trans. Information Forensics, and Security, vol. 13, no. 11, pp. 2831–2843, Nov. 2018.
  • N. Jiang, Y. Deng, N. Arumugam, X. Kang, and T. Q. S. Quek, “Analyzing Random Access Collisions in Massive IoT Networks,” IEEE Trans. Wireless Commun., vol. 17, no. 10, pp. 6853–6870, Oct. 2018.
  • J. Yuan, A. Huang, H. Shan, T. Q. S. Quek, and G. Yu, “Design and Analysis of Random Access for Standalone LTE-U Systems,” IEEE Trans. Vehicular Techno., vol. 67, no. 10, pp. 9347–9361, Oct. 2018.

big data

Big Data
While big data can be definitely perceived as a big blessing, big challenges also arise with large-scale datasets. Due to their disparate origins, the resultant datasets are often incomplete and include a sizable portion of missing entries. In addition, massive datasets are noisy, prone to outliers, and vulnerable to cyber-attacks. These effects are amplified if the acquisition and transportation cost per datum is driven to a minimum. Some recent contributions include:

  • Y. Fu, Z. Yang, T. Q. S. Quek, and H. Yang, “Towards Cost Minimization for Wireless Caching Net- works with Recommendation and Uncharted Users’ Feature Information,” IEEE Trans. Wireless Commun., submitted.
  • Y. Fu, L. Salaun, X. Yang, W. Wen, and T. Q. S. Quek, “Caching Efficiency Maximization for Device- to-Device Communication Networks: A Recommend to Cache Approach,” IEEE Trans. Wireless Commun., submitted.
  • Y. Fu, Q. Yu, T. Q. S. Quek, and W. Wen, “Revenue Maximization for Content-oriented Wireless Caching Networks (CWCNs) with Repair and Recommendation Considerations,” IEEE Trans. Wireless Commun., revised.
  • Y. Yuan, D. Soh, H. Yang, and T. Q. S. Quek, “Learning Overlapping Community-based Networks,” IEEE Trans. Signal and Information Processing over Networks,, vol. 5, no. 4, pp. 684-697, Dec. 2019.
  • Q. D. La, T. Q. S. Quek, and H. Shin, “Dynamic Network Formation Game with Social Awareness in Device-to-Device Communications,” IEEE Trans. Wireless Commun., vol. 17, no. 10, pp. 6544-6558, Oct. 2018.
  • Y. Meng, C. Jiang, Z. Han, T. Q. S. Quek, and Y. Ren, “Social Learning Based Inference for Crowd Sensing in Mobile Social Networks,” IEEE Trans. Mobile Computing, vol. 17, no. 8, pp. 1966–1979, Aug. 2018.
  • J. Song, M. Sheng, T. Q. S. Quek, C. Xu, and X. Wang, “Learning Based Content Caching and Sharing for Wireless Networks,” IEEE Trans. Commun., vol. 65, no. 10, pp. 4309-4324, Oct. 2017.

Comments are closed.