Joint Sensing and Communication technology is one of the key 6G technologies. It makes your phone/vehicle/device, etc., smarter with the function of sensing and communication simultaneously [1]. We target the design of sensing and communication by formulating an optimization problem, especially in MAC layer or PHY layer, e.g. resource allocation, beamforming design. In this thesis, we aim to solve the formulated optimization problem using machine learning algorithms. Examples can be found in [2-3]. If you are interested in sensing and communication related optimization problems based on machine learning, please contact me.

Research objective: 1. Search for new problems in sensing and communication area. 2. Solve the problem with machine learning tools. 3. If possible, publish the results.

Expected gain of knowledge: Wireless communication, machine learning-based optimization

[1] Y. Cui, F. Liu, X. Jing and J. Mu, "Integrating Sensing and Communications for Ubiquitous IoT: Applications, Trends, and Challenges," in IEEE Network, vol. 35, no. 5, pp. 158-167, September/October 2021.

[2]. J. Zhang, C. Masouros, F. Liu, Y. Huang and A. L. Swindlehurst, "Low-Complexity Joint Radar-Communication Beamforming: From Optimization to Deep Unfolding," in IEEE Journal of Selected Topics in Signal Processing.

[3]. P. Jiang, M. Li, R. Liu, W. Wang and Q. Liu, "Joint Waveform and Beamforming Design in RIS-ISAC Systems: A Model-Driven Learning Approach," in IEEE Transactions on Communications.