Research

Research topics include wireless communications and networking, network intelligence, big data processing, IoT, and wireless security.


Heterogeneous Networks
The ever-increasing need for higher data rates and multimedia services leads to stringent requirements on the area spectral efficiency that next-generation cellular wireless networks are expected to deliver. A promising approach to solving this problem is through the deployment of heterogeneous networks, which represent a novel networking paradigm based on the idea of deploying short-range, low-power, and low-cost base stations operating in conjunction with the main macro-cellular network infrastructure.
Some contributions include:

  • 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., 2019.
  • Q. Zhang, H. Yang, T. Q. S. Quek, and J. Lee, “Heterogeneous Cellular Networks with LoS and NLoS Transmissions: The Role of Massive MIMO and Small Cells,” IEEE Trans. Wireless Commun., vol. 16, no. 12, pp. 7996-8010, Dec. 2017.
  • H. Yang, G. Geraci, Y. Zhong, and T. Q. S. Quek, “Packet Throughput Analysis of Static and Dynamic TDD in Small Cell Networks,” IEEE Wireless Commun. Letters, vol. 6, no. 6, pp. 742-745, Dec. 2017.
  • Y. Zhong, T. Q. S. Quek, and X. Ge, “Heterogeneous Cellular Networks with Spatio-Temporal Traffic: Delay Analysis and Scheduling,” IEEE J. Select Areas Commun., vol. 35, no. 6, pp. 1373-1386, Jun. 2017.
  • Z. Chen, J. Lee, T. Q. S. Quek, and M. Kountouris, “Cooperative Caching and Transmission Design in Cluster-Centric Small Cell Networks,” IEEE Trans. Wireless Commun., vol. 16, no. 5, pp. 3401-3415, May 2017.
  • H. Sun, M. Sheng, M. Wildemeersch, and T. Q. S. Quek, “Traffic Adaptation in Energy Harvesting Small Cell Networks with Dynamic TDD,” IEEE J. Select Areas Commun., vol. 34, no. 12, pp. 3234-3251, Dec. 2016.
  • H. Yang, G. Geraci, and T. Q. S. Quek, “Energy Efficient Design of MIMO Heterogeneous Networks with Wireless Backhaul,” IEEE Trans. Wireless Commun., vol. 15, no. 7, pp. 4914-4927, Jul. 2016. 
  • G. Zhang, T. Q. S. Quek, M. Kountouris, A. Huang, and H. Shan, “Fundamentals of Heterogeneous Backhaul Design – Optimization and Analysis,” IEEE Trans. Commun., vol. 64, no. 2, pp. 876-889, Feb. 2016.
  • G. Zhang, T. Q. S. Quek, A. Huang, and H. Shan, “Delay and Reliability Tradeoffs in Heterogeneous Cellular Networks,” IEEE Trans. Wireless Commun., vol. 15, no. 2, pp. 1101-1113, Feb. 2016.
  • H. Yang, J. Lee, and T. Q. S. Quek, “Heterogeneous Cellular Network with Energy Harvesting based D2D Communications,” IEEE Trans. Wireless Commun., vol. 15, no. 2, pp. 1406-1419, Feb. 2016.
  • P.-S. Yu, J. Lee, T. Q. S. Quek, and Y.-W. Peter Hong, “Traffic Offloading in Heterogeneous Networks with Energy Harvesting Personal Cells – Network Throughput and Energy Efficiency,” IEEE Trans. Wireless Commun., vol. 15, no. 2, pp. 1146-1161, Feb. 2016.
  • H. Sun, M. Wildemeersch, M. Sheng, and T. Q. S. Quek, “D2D Enhanced Heterogeneous Cellular Networks with Dynamic TDD,” IEEE Trans. Wireless Commun., vol. 14, no. 8, pp. 4204-4218, Aug. 2015.
  • D. Chen, T. Q. S. Quek, and M. Kountouris, “Backhauling in Heterogeneous Cellular Networks – Modeling and Tradeoffs,” IEEE Trans. Wireless Commun., vol. 14, no. 6, pp. 3194-3206, Jun. 2015.
  • J. Lee and T. Q. S. Quek, “Hybrid Full-/Half-Duplex System analysis in Heterogeneous Wireless Networks,” IEEE Trans. Wireless Commun., vol. 14, no. 5, pp. 2883-2895, May 2015.
  • M. Wildemeersch, T. Q. S. Quek, M. Kountouris, A. Rabbachin, and C. H. Slump, “Successive Interference Cancellation in Heterogeneous Cellular Networks,” IEEE Trans. Commun., vol. 62, no. 12, pp. 4440–4453, Dec. 2014.
  • Y. Yang, T. Q. S. Quek, and L. Duan, “Backhaul-Constrained Small Cell Networks: Refunding and QoS Provisioning,IEEE Trans. Wireless Commun., vol. 13, no. 9, pp. 5148-5161, Sep. 2014.
  • Y. Yang and T. Q. S. Quek, “Optimal Subsidies for Shared Small Cell Networks – A Social Network Perspective,” IEEE J. Select. Topics Signal Processing, vol. 8, no. 4, pp. 690-702, Aug. 2014.
  • Y. S. Soh, T. Q. S. Quek, M. Kountouris, and G. Caire, “Cognitive Hybrid Division Duplex for Two-Tier Femtocell Networks,” IEEE Trans. Wireless Commun., vol. 12, no. 10, pp. 4852-4865, Oct. 2013.
  • M. Wildemeersch, T. Q. S. Quek, A. Rabbachin, and C. H. Slump, “Cognitive Small Cell Networks: Energy Efficiency and Trade-Offs,” IEEE Trans. Commun., vol. 61, no. 9, pp. 4016–4029, Sep. 2013.
  • Y. S. Soh, T. Q. S. Quek, M. Kountouris, and H. Shin, “Energy Efficient Heterogeneous Cellular Networks,” IEEE J. Select. Areas Commun., vol. 31, no. 5, pp. 840–850, May 2013.
  • W. C. Cheung, T. Q. S. Quek, and M. Kountouris, “Throughput Optimization, Spectrum Allocation, and Access Control in Two-Tier Femtocell Networks,” IEEE J. Select. Areas Commun., vol. 30, no. 3, pp. 561–574, Apr. 2012.
  • D. Lopez-Perez, I. Guvenc, G. de la Roche, M. Kountouris, T. Q. S. Quek, and J. Zhang, “Enhanced Inter-Cell Interference Coordination Challenges in Heterogeneous Networks,” IEEE Wireless Commun. Mag., vol. 18, no. 3, pp. 22–30, Jun. 2011.

c-ran_base_stationsCloud Radio Access Networks 
Cloud computing technology has emerged as a promising solution for providing high energy efficiency together with gigabit data rates across software defined wireless communication networks, in which the virtualization of communication hardware and software elements place stress on communication networks and protocols. Consequently, cloud radio access networks have been proposed as cost-effective potential solutions to alleviating inter-tier interference and improving cooperative processing gains in HetNets through combination with cloud computing.
Some contributions include:

  • J. Tang, T. Q. S. Quek, T.-H. Chang, and B. Shim, “Systematic Resource Allocation in Cloud RAN with Caching as a Service under Two Timescales,” IEEE J. Selected Areas Commun., revised.
  • W. Xia, J. Zhang, T. Q. S. Quek, S. Jin, and H. Zhu, “Joint Optimization of Fronthaul Compression and Bandwidth Allocation in Uplink H-CRAN with Large System Analysis,” IEEE Trans. Wireless Commun., 2018.
  • Q. He, T. Q. S. Quek, Z. Chen, and S. Li, “Compressive Channel Estimation and Multiuser Detection in C-RAN with Low-Complexity Methods,” IEEE Trans. Wireless Commun., 2018.
  • K. Guo, M. Sheng, J. Tang, T. Q. S. Quek, and Z. Qiu, “Interplay between Communication and Computation: A Joint Design for Green C-RAN,” IEEE Trans. Commun., 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.
  • Y. Zhong, T. Q. S. Quek, and W. Zhang, “Complementary Networking for C-RAN: Spectrum Efficiency, Delay and System Cost,” IEEE Trans. Wireless Commun., vol. 16, no. 7, pp. 4639-4653, Jul. 2017.
  • J. Tang, W. P. Tay, T. Q. S. Quek, and B. Liang, “System Cost Minimization in Cloud RAN with Limited Fronthaul Capacity,” IEEE Trans. Wireless Commun., vol. 16, no. 5, pp. 3371-3384, May 2017.
  • K. Guo, M. Sheng, J. Tang, and T. Q. S. Quek, “Exploiting Hybrid Clustering and Computation Provisioning for Green C-RAN,” IEEE J. Select. Areas Commun., vol. 34, no. 12, pp. 4063-4076, Dec. 2016.
  • T. X. Vu, H. D. Nguyen, T. Q. S. Quek, and S. Sun, “Adaptive Cloud Radio Access Networks – Compression and Optimization,” IEEE Trans. Signal Processing, vol. 65, no. 1, pp. 228-241, Jan. 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.
  • J. Liu, M. Sheng, T. Q. S. Quek, and J. Li, “D2D Enhanced Coordinated Multipoint for Cloud Radio Access Networks,” IEEE Trans. Wireless Commun., vol. 15, no. 6, pp. 4248-4262, Jun. 2016.
  • T. X. Vu, H. D. Nguyen, and T. Q. S. Quek, “Adaptive Compression and Joint Decoding for Fronthaul Uplinks in Cloud Radio Access Networks,” IEEE Trans. Commun., vol. 63, no. 11, pp. 4565-4575, Nov. 2015.
  • J. Zhao, T. Q. S. Quek, and Z. Lei, “Heterogeneous Cellular Networks with Wireless Backhaul – Fast Admission Control and Large System Analysis,” IEEE J. Select. Areas Commun., vol. 33, no. 10, pp. 2128-2143, Oct. 2015.
  • J. Tang, W. P. Tay, and T. Q. S. Quek, “Cross-Layer Resource Allocation with Elastic Service Scaling in Cloud Radio Access Network,” IEEE Trans. Wireless Commun., vol. 14, no. 9, pp. 5068-5081, Sep. 2015.
  • J. Zhao, T. Q. S. Quek, and Z. Lei, “Coordinated Multipoint Transmission with Limited Backhaul Data Transfer Constraints,” IEEE Trans. Wireless Commun., vol. 12, no. 6, pp. 2762-2775, Jun. 2013.

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 contributions include:

  • T. Q. Dinh, Q. D. La, T. Q. S. Quek, and H. Shin, “Dynamic Mult-User Computation Offloading in Mobile Edge Computing,” IEEE Trans. Commun., 2019.
  • 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.

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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. This interaction includes not just sensing and data analytics but also simultaneous actuation of billions of connected devices. Some contributions include:

  • C. She, Z. Chen, C. Yang, T. Q. S. Quek, Y. Li, and B. Vucetic, “Improving Network Availability of URLLC via Multi-Connectivity,” IEEE Trans. Wireless Commun., 2018.
  • 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., 2018.
  • M. Ngo, Q. D. La, D. Leong, and T. Q. S. Quek, “Adaptive MAC Scheduling for Body Sensor Networks: A User-Behavior-Driven Approach,” IEEE Internet of Things Journal, revised.
  • 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., revised.
  • 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, 2018.
  • 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., 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • E. Steinmetz, M. Wildemeersch, T. Q. S. Quek, and H. Wymeersch, “Reception Probabilities in Vehicular Communications Close to Intersections,” IEEE Trans. Mobile Computing, revised.
  • 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.

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. Overall, big data present challenges in which resources such as time, space, and energy, are intertwined in complex ways with data resources. Some contributions include:

  • Y. Yuan, D. Soh, H. Yang, and T. Q. S. Quek, “Learning Overlapping Community-based Networks,” IEEE J. Selected Areas Commun., submitted.
  • 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., 2019.
  • K. H. Doan, T. V. Nguyen, T. Q. S. Quek, and H. Shin, “Content-Aware Proactive Caching for Backhaul Offloading in Cellular System,” IEEE Trans. Wireless Commun., vol. 17, no. 5, 3128-3140, May 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.

Cognitive Radio
With the proliferation of radio devices and communication services, multiple systems sharing a common spectrum must coexist. Conventional static spectrum management may not be suitable for such dynamic systems and may not lead to efficient system utilization.
This imbalance between the spectrum scarcity and low utilization motivates the concept of cognitive radio networks. A cognitive radio is designed to utilize the wireless spectrum efficiently while maximizing its own spectral efficiency under interference-limited regime. Some contributions include:

  • H. He, H. Shan, A. Huang, L. X. Cai, and T. Q. S. Quek, “Proportional Fairness-based Resource Allocation for LTE-U coexisting with Wi-Fi,” IEEE Access, vol. 5, pp. 4720-4731, 2017.
  • X. Wang, T. Q. S. Quek, M. Sheng, and J. Li, “Throughput and Fairness Analysis of Wi-Fi and LTE-U in Unlicensed Band,” IEEE J. Select. Areas Commun., vol. 35, no. 1, pp. 63-78, Jan. 2017.
  • J. Li, X. Wang, D. Feng, M. Sheng, and T. Q. S. Quek, “Share in the Commons: Coexistence between LTE Unlicensed and WiFi,” IEEE Wireless Commun. Mag., vol. 23, no. 6, pp. 16-23, Dec. 2016.
  • G. Zhang, A. Huang, H. Shan, J. Wang, T. Q. S. Quek, and Y.-D. Yao, “Design and Analysis of Channel  Access in Multi-Channel Cognitive Radio Systems with Delay Constraint,” IEEE J. Select. Areas Commun., vol. 32, no. 11, pp. 2026–2038, Nov. 2014.
  • T. V. Nguyen, H. Shin, T. Q. S. Quek, and M. Z. Win, “Sensing and Probing Cardinalities for Active Cognitive Radios,” IEEE Trans. Signal Processing, vol. 60, no. 4, pp. 1833–1848, Apr. 2012.
  • A. Rabbachin, T. Q. S. Quek, H. Shin, and M. Z. Win, “Cognitive Network Interference,” IEEE J. Select. Areas Commun., vol. 29, no. 2, pp. 480–493, Feb. 2011.

Wireless Security
Broadcast nature of wireless medium makes wireless networks susceptible to eavesdropping, and hence secure transmission is a fundamental issue in such networks. Traditionally, this has been addressed by employing cryptographic protocols that are believed to be computationally hard for the adversary to decipher.
For instance, eavesdropping is extremely easy since anyone within communication range can listen to the traffic in the air, and possibly extract information. Unlike conventional cryptographic security mechanisms, the main idea of the physical-layer security is to exploit the unique properties of wireless medium to provide ways of combating security threats. Some contributions include:

  • D. He, C. Liu, H. Wang, and T. Q. S. Quek, “Learning Mobile Eavesdropper for Wireless Powered Secure Transmission,” IEEE Trans. Wireless Commun., submitted.
  • C. Liu, J. Lee, and T. Q. S. Quek, “Safeguarding UAV Communications against Full-duplex Active Eavesdropper,” IEEE Trans. Wireless Commun., revised.
  • 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. , no. , pp. -, Nov. 2018.
  • T. Chan, J. Lee, J. Prakash, and T. Q. S. Quek, “Secret Group-Key Generation at Physical Layer for Multi- Antenna Mesh Topology,” IEEE Trans. Inform. Forensics and Security, vol. 14, no. 1, pp. 18-33, Jan. 2019.
  • K. Lee, J.-P. Hong, H.-H. Choi, and T. Q. S. Quek, “Wireless-Powered Two-Way Relaying Protocols for Optimizing Physical Layer Security,” IEEE Trans. Information Forensics, and Security, vol. 14, no. 1, pp. 162–174, Jan. 2019.
  • 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.
  • P. Mohapatra, N. Pappas, J. Lee, T. Q. S. Quek, and V. Angelakis, “Secure Communications for the Two-user Broadcast Channel with Random Traffic,” IEEE Trans. Inform. Forensics and Security, vol. 13, no. 9, pp. 2294-2309, Sep. 2018.
  • 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.
  • R.-H. Hsu, J. Lee, T. Q. S. Quek, and J.-C. Chen, “GRAAD: Group Anonymous and Accountable D2D Communication in Mobile Networks,” IEEE Trans. Inform. Forensics and Security, vol. 13, no. 2, pp. 449-464, Feb. 2018.
  • M. Zhao, J. Y. Ryu, J. Lee, T. Q. S. Quek, and S. Feng, “Exploiting Trust Degree for Multiple-Antenna User Cooperation,” IEEE Trans. Wireless Commun., vol. 16, no. 8, pp. 4908-4923, Aug. 2017.
  • H. M. Wang, C. Wang, T. X. Zheng, and T. Q. S. Quek, “Impact of Artificial Noise on Cellular Networks: A Stochastic Geometry Approach,” IEEE Trans. Wireless Commun., vol. 15, no. 11, pp. 7390-7404, Nov. 2016.
  • J.-Y. Ryu, J. Lee, and T. Q. S. Quek, “Transmission Strategy against Opportunistic Attack for MISO Secure Channels,” IEEE Commun. Letters, vol. 20, no. 11, pp. 2304-2307, Nov. 2016.
  • J.-Y. Ryu, J. Lee, and T. Q. S. Quek, “Confidential Cooperative Communication with Trust Degree of Potential Eavesdroppers,” IEEE Trans. Wireless Commun., vol. 15, no. 6, pp. 3823-3836, Jun. 2016.
  • Q. D. La, T. Q. S. Quek, J. Lee, S. Jin, and H. Zhu, “Deceptive Attack and Defense Game in Honeypot-enabled Networks for the Internet of Things,” IEEE Internet of Things Journal, vol. 3, no. 6, pp. 1015-1035, Dec. 2016.
  • C. D. T. Thai, J. Lee, and T. Q. S. Quek, “Physical-Layer Secret Key Generation with Colluding Untrusted Relays,” IEEE Trans. Wireless Commun., vol. 15, no. 2, pp. 1517-1530, Feb. 2016.
  • J. Wang, J. Lee, F. Wang, and T. Q. S. Quek, “Jamming-Aided Secure Communication in Massive MIMO Rician Channels,” IEEE Trans. Wireless Commun., vol. 14, no. 12, pp. 6854-6868, Dec. 2015.
  • S. H. Chae, J. H. Lee, W. Choi, and T. Q. S. Quek, “Enhanced Secrecy in Stochastic Wireless Networks: Artificial Noise with Secrecy Protected Zone,” IEEE Trans. Inform. Forensics and Security, vol. 9, no. 10, pp. 1617-1628, Oct. 2014.
  • Y. Jeong, T. Q. S. Quek, J. S. Kwak, and H. Shin, “Multicasting in Stochastic MIMO Network,” IEEE Trans. Wireless Commun., vol. 13, no. 4, pp. 1729-1741, Apr. 2014.

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