Seminars

02/06/2020: ThS. Nguyễn Văn Lý (San Diego State Univ., US), SVM-based Channel Estimation and Data Detection for Massive MIMO Systems with One-Bit ADCs

The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much more challenging. In this talk, we show how Support Vector Machine (SVM), a well-known supervised learning technique in machine learning can be exploited to provide efficient and robust channel estimation and data detection in massive MIMO systems with one-bit ADCs. First, we propose SVM-based channel estimation methods for both uncorrelated and spatially correlated channels. Next, a two-stage detection algorithm is proposed where SVM is further exploited in the first stage. We also propose an SVM-based Joint Channel Estimation and Data Detection (CE-DD) method and an extension to OFDM systems with frequency-selective fading channels. Simulation results show that the proposed methods are efficient, robust, and also outperform existing ones.

Speaker: Mr. Nguyen Van Ly, San Diego State Univ.

Time: 15:30, Tuesday, June 02, 2020

Venue: Webinar – Microsoft Teams

Ly V. Nguyen received the B.Eng. degree in electronics and telecommunications from the University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam, in 2014, and the M.Sc. degree in advanced wireless communications systems from The CentraleSupélec, University of Paris-Saclay, France, in 2016. He is currently pursuing a Ph.D. degree in a joint doctoral program in computational science with San Diego State University and The University of California, Irvine, CA, USA. He received a Best Paper Award at the IEEE ICC 2020 as the first author. His research interests include wireless communications, signal processing, and machine learning.

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