Abstract: Emerging applications, such as self-driving cars and Internet of Things (IoT), require wireless communications at extremely high-rates. In order to support such applications and improve existing ones, multi-antenna communication systems will leverage beamforming to communicate with multiple user equipments at the same time and in the same frequency band. The data rates can be increased even further by communicating at millimeter-wave (mmWave) frequencies, where large portions of contiguous bandwidth are available. In this talk, I focus on channel estimation, a key task for fine-grained beamforming at mmWave frequencies. Specifically, I present novel channel-vector denoising algorithms for multi-antenna basestation designs that rely on 1-bit analog-to-digital converters (ADCs) to reduce system costs and power. I then propose blind estimators to efficiently track key quantities for denoising, such as noise power and signal power. Finally, I present a nonparametric channel denoising algorithm that can be utilized in a wide range of emerging wireless communication systems.

Bio: Alexandra Gallyas-Sanhueza is a PhD candidate in Electrical and Computer Engineering at Cornell University. Previously, she worked as a Visiting Scientist at the Cornell Wilson Synchrotron Laboratory. Alexandra received her B.Sc. degree in Electrical Engineering from Pontificia Universidad Catolica de Chile in 2015. She has completed summer internships at the Analog Garage (Analog Devices) and at IBM Research, and will return to IBM Research as an intern for the summer of 2023. In 2019, she was a co-recipient of the Qualcomm Innovation Fellowship.