Research

Current research themes

Future wireless communication systems should provide trustworthy transmission services to
various emerging applications of vertical industries, e.g. intelligent transportation, digital health,
smart factory, and smart agriculture. They should reliably connect various physical entities to each
other and to the Internet for real-time data collection, decision-making, and optimal control. They
should operate in the most environment-friendly fashion to minimize the ecological impact. They
should achieve truly ubiquitous coverage, extending broadband connectivity well beyond developed
metropolitan areas to underdeveloped remote communities, from macroscopic devices/equipment to
nanoscale sensors, from traditional terrestrial users to prospering aerial terminals. To best satisfy
these generally conflicting requirements with limited spectrum and energy resources, future wireless
systems must be furnished with a high level of intelligence.

With the generous funding support from NSERC, CFI, BCKDF, and industrial partners, we are developing intelligent wireless transmission solutions for our hyper-connected future. Building up our extensive expertise in wireless transmission technologies (see below), we are actively working along the following three theme directions:

• Data-oriented session-specific intelligent design for trustworthy and sustainable connectivity: Mission-critical applications, such as autonomous driving and smart manufacturing, require ultra/hyper-reliable low-latency communication services. To achieve trustworthy connectivity with most efficient resource utilization, we develop an innovative data-oriented session-specific design approach to design highly intelligent wireless transceiver structure for future wireless systems. 

• Non-terrestrial multiband transmissions for ubiquitous broadband connectivity: There is an urgent need to provide affordable broadband Internet access to remote and underdeveloped communities to mitigate the digital divide. Non-terrestrial networks (NTNs) through satellite constellations and high-altitude platforms (HAP) serve as attractively solution for ubiquitous coverage. To effectively reduce the cost per bit, we will exploit the huge unlicensed spectrum of terahertz (THz) and optical bands to develop resilient terabits transmission solutions.

• Joint scheduling and transmission design for efficient real-time sensory data collection: Digital twin technologies create and maintain virtual replicas of physical systems to facilitate their design, monitoring, and control. The virtual replicas need to track the evolution of their physical counterpart through timely status update. With the application of advanced machine learning algorithms, we develop efficient solutions to facilitate timely data collection from resource constraint sensors.

We explore advanced machine learning (ML) techniques in the design and analysis of innovative transmission solutions. Specifically, we integrate model-based offline learning with model-free online tuning for transmission scheme design, to maintain near optimal performance during online operation while addressing the sample inefficiency problem of deep reinforcement learning (DRL). We explore the hierarchical structure of large action spaces of joint scheduling and transmission scheme selection problems, to facilitate effective transfer learning and improve learning efficiency. More recently, we develop advanced multi-objective reinforcement learning (MORL) solutions to navigate various inherent tradeoffs in wireless transmission systems. These innovative approaches not only lead to efficient transmission system design, also stimulate further development of machine learning techniques.

• T. Rakia, F. Gebali, H.-C. Yang, and M.-S. Alouini, “Cross layer analysis of point-to-multi-point hybrid FSO/RF networks”, Journal of Optical Communications and Network, Vol. 9, no. 3, pp. 234-243, March 2017.
• T. Rakia, H.-C. Yang, F. Gebali, and M.-S. Alouini, “Optimal design of dual-hop VLC/RF communication system with energy harvesting”, IEEE Communication Letters, Vol. 20, no. 10, pp. 1979 – 1982, October 2016.
• M. Usman, H.-C. Yang, and M.-S. Alouini, “Practical switching based hybrid FSO/RF transmission and its performance analysis”, IEEE Photonics Journal, Vol. 6, no. 5, 2902713, October 2014.

• T. Bao, H.-C. Yang, and M. O. Hasna, “On physical layer secrecy performance of multiple antenna transmission with partial legitimate user CSI,” IET Communications, Vol. 13, no. 5, pp. 2285-2295, September 2019.
• Liao, Z., L. Yang, J. Chen, H.-C. Yang, and M.-S. Alouini, “Physical layer security for dual-hop VLC/RF communication systems,” IEEE Communications Letters, Vol. 22, no. 12, pp. 2603-2606, December 2018.

• W.-J. Wang and H.-C. Yang, “Effect of imperfect spectrum sensing on slotted secondary transmission: energy efficiency and queuing performance”, IEEE Transactions on Cognitive Communications and Networking, Vol. 4, no. 4, pp. 764-772, December 2018.
• M. Hanif, H.-C. Yang, and M.-S. Alouini, “Transmit antenna selection for power adaptive underlay cognitive radio with instantaneous interference constraint”, IEEE Transactions on Communications, Vol. 65, no. 6, pp. 2357–2367, June 2017.
• M. Usman, H.-C. Yang, and M.-S. Alouini, “Extended delivery time analysis for cognitive packet transmission with application to secondary queuing analysis”, IEEE Transactions on Wireless Communications, Vol. 14, no. 10, pp. 5300-5312, October 2015.

Massive MIMO Transmission

Massive MIMO technology can achieve very high spectral and power efficiencies. Meanwhile, implementing a large number entails very high hardware and computational complexity. In this project, we develop practical low-complexity solutions for massive MIMO systems and characterize their performance versus complexity tradeoff.

• M. Hanif, H.-C. Yang, G. Boudreau, E. Sich, and H. Seyedmehdi, “Antenna subset selection for massive MIMO systems: a trance-based sequential approach for sum rate maximization”, Journal of Communications and Networks, Vol. 20, no. 2, pp. 144-155, April 2018.
• M. Hanif, H.-C. Yang, G. Boudreau, E. Sich, and H. Seyedmehdi, “Low-complexity hybrid precoding for multi-user massive MIMO systems: A hybrid EGT/ZF approach”, IET Communications, Vol. 11, no. 5, pp. 765-771, April 2017.