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The wireless communication systems beyond 5G aim to enhance connectivity with a drastic increase in the number of connected devices and improved quality of service requirements in terms of data rate, latency, reliability, and scalability. To this end, we need new physical layer signal processing for the futuristic systems to efficiently acquire and process the resulting enormous amount of data. An important emerging approach is the deep learning-based techniques. The state-of-the-art deep learning solutions for wireless communications are indeed very promising due to their learning abilities. However, they lack interpretability and come with no performance guarantees. In this PhD project, we will investigate the integration of the traditional signal processing algorithms with performance guarantees, and the tools from deep learning, combining the best of both worlds. The challenge here is to understand the right balance between the model-driven and data-driven methods. Our goal is to explore the different combinations of the two approaches and develop new solutions for signal detection and channel acquisition. We also characterize the performance limits of this hybrid analytical framework, aiming at new wireless system designs with improved reliability and connectivity.
The Circuits and System Group seeks for enthusiastic PhD candidates to work on this project. The research will focus on
To apply, please follow the process indicated on the formal vacancy announcement.