Research

Research topics include wireless communications and networking, network intelligence, URLLC, big data processing, IoT, and beyond 5G.


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. Yang, Z. Liu, T. Q. S. Quek, and H. V. Poor, “Scheduling Policies for Federated Learning in Wireless Networks,” IEEE Trans. Commun., 2020.
  • H. H. Yang, A. Arafa, T. Q. S. Quek, and H. V. Poor, “Optimizing Information Freshness in Wireless Networks: A Stochastic Geometry Approach,” IEEE Trans. Mobile Computing, revised.
  • H. Lee, T. Q. S. Quek, and S. H. Lee, “A Deep Learning Approach to Universal Binary Communication Transceiver,” IEEE Trans. Wireless Commun., 2020.
  • H. S. Jang, H. Lee, T. Q. S. Quek, and H. Shin, “Deep Learning Based Random Access Framework,” IEEE Trans. Wireless Commun., submitted.
  • 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.
  • H. Yang and T. Q. S. Quek, “Spatiotemporal Analysis for SINR Coverage in Small Cell Networks,” IEEE Trans. Commun., vol. 67, no. 8, pp. 5520-5531, Aug. 2019.
  • H. Lee, S. H. Lee, T. Q. S. Quek, and I. Lee, “Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications,” IEEE Commun. Mag., vol. 57, no. 3, pp. 35-41, Mar. 2019.
  • D. He, C. Liu, T. Q. S. Quek, and H. Wang, “Transmit Antenna Selection in MIMO Wiretap Channels: A Machine Learning Approach,” IEEE Wireless Commun. Letters, vol. 7, no. 4, pp. 634–637, Aug. 2018.
  • H. Lee, I. Lee, T. Q. S. Quek, and S. H. Lee, “Binary Signalling Design for Visible Light Communication: A Deep Learning Framework,” Opt. Express, vol. 24, no. 14, pp. 18131–18142, Jul. 2018.
  • K. N. Doan, T. V. Nguyen, T. Q. S. Quek, and H. Shin, “Content-Aware Proactive Caching for Backhaul Offloading in Cellular Network,” IEEE Trans. Wireless Commun., vol. 17, no. 5, pp. 3128–3140, May 2018.
  • 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:

  • M. Siew, D. Cai, L. Li, and T. Q. S. Quek, “Dynamic Pricing for Resource Quota Sharing in Mobile Edge Computing,” IEEE J. Select. Areas Commun., submitted.
  • L. Li, M. Siew, T. Q. S. Quek, J. Ren, Z. Chen, and Y. Zhang, “Learning-Based Priority Pricing for Job Offloading in Mobile Edge Computing,” IEEE Trans. Wireless Commun., submitted.
  • R. 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 IoT Journal, revised.
  • 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. Ngo, H. Shin, Q. D. La, T. Q. Dinh, T. Q. S. Quek, “Enabling Intelligence in Fog Computing to Achieve Energy and Latency Reduction,” Digital Commun. and Networks, vol. 5, no. 1, pp. 3-9, Feb. 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.
  • T. Q. Dinh, Q. D. La, T. Q. S. Quek, and H. Shin, “Learning for Computation Offloading in Mobile Edge Computing,” IEEE Trans. Commun., vol. 66, no. 12, pp. 6353-6367, Dec. 2018.
  • K. Guo, M. Sheng, J. Tang, T. Q. S. Quek, and Z. Qiu, “On the Interplay between Communication and Computation in Green C-RAN with Limited Fronthaul and Computation Capacity,” IEEE Trans. Commun., vol. 66, no. 7, pp. 3201–3216, Jul. 2018.
  • 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.
  • J. Tang, R. Wen, T. Q. S. Quek, and M. Peng, “Fully Exploiting Cloud Computing to Achieve a Green and Flexible C-RAN,” IEEE Commun. Mag., vol. 55, no. 11, pp. 40-46, Nov. 2017.
  • 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.
  • J. Tang and T. Q. S. Quek, “The Role of Cloud Computing in Content-Centric Mobile Networking,” IEEE Commun. Mag., vol. 54, no. 8, pp. 52-59, Aug. 2016.

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:

 

  • 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, 2019.
  • C. She, C. Liu, T. Q. S. Quek, C. Yang, and Y. Li, “Ultra-reliable and Low-latency Communications in Unmanned Aerial Vehicle Communication Systems,” IEEE Trans. Commun., vol. 67, no. 5, pp. 3768-3781,  May 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.
  • J. Tang, B. Shim, and T. Q. S. Quek, “Service Multiplexing and Revenue Maximization in Sliced C- RAN Incorporated with URLLC and Multicast eMBB,” IEEE J. Select. Areas Commun., vol. 37, no. 4, pp. 881–895, Apr. 2019. 
  • C. Sun, C. She, C. Yang, T. Q. S. Quek, Y. Li, and B. Vucetic, “Optimizing Resource Allocation in Short Blocklength Regime for Ultra-reliable and Low-latency Communications,” IEEE Trans. Wireless Commun., vol. 18, no. 1, pp. 402-415, Jan. 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, Z. Chen, C. Yang, T. Q. S. Quek, Y. Li, and B. Vucetic, “Improving Network Availability of Ultra-Reliable and Low-Latency Communications via Multi-Connectivity,” IEEE Trans. Commun., vol. 66, no. 11, pp. 5482–5496, Nov. 2018.
  • C. She, C. Yang, and T. Q. S. Quek, “Joint Uplink and Downlink Resource Configuration for Ultra- reliable and Low-latency Communications,” IEEE Trans. Commun., vol. 66, no. 5, pp. 2266–2280, May 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.
  • Y. Zhong, M. Haenggi, T. Q. S. Quek, and W. Zhang, “On the Stability of Static Poisson Networks under Random Access,” IEEE Trans. Commun., vol. 64, no. 7, pp. 2985-2998, Jul. 2016.

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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 IoT Journal, revised.
  • 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, 2019.
  • H. S. Jang, H. Jin, B. C. Jung, and Tony Q. S. Quek, “Versatile Access Control for Massive IoT: Throughput, Latency, and Energy Efficiency,” IEEE Trans. Mobile Computing, 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.
  • Q. D. La, N.-N. Duong, M. V. Ngo, H. T. Hoang, and T. Q. S. Quek, “Dense Deployment of BLE-based Body Area Networks: A Coexistence Study,” IEEE Trans. Green Commun. Networking, vol. 2, no. 4, pp. 972-981, Dec. 2018.
  • 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.
  • J. Li, A. Huang, H. Shan, H. Yang, and T. Q. S. Quek, “Analysis of Packet Throughput in Small Cell Networks under Clustered Dynamic TDD,” IEEE Trans. Wireless Commun., vol. 17, no. 9, pp. 5729– 5742, Sep. 2018.
  • J. Wang, C. Jiang, K. Zhang, T. Q. S. Quek, Y. Ren, and L. Hanzo, “Vehicular Sensing Networks in a Smart City: Principles, Technologies, and Applications,” IEEE Wireless Commun. Mag., vol. 66, no. 2, pp. 601-614, Feb. 2018.
  • J. Yuan, H. Shan, A. Huang, T. Q. S. Quek, and Y.-D. Yao, “Massive Machine-to-Machine Communications in Cellular Network: Distributed Queueing Random Access meets MIMO,” IEEE Access, vol. 5, no. 1, pp. 2981-2993, 2017.

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. Yuan, D. Soh, H. Yang, and T. Q. S. Quek, “Learning Overlapping Community-based Networks,” IEEE Trans. Signal and Information Processing over Networks,, 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.

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