Van Ly 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, Paris-Saclay University, France, in 2016. He is currently pursuing the Ph.D. degree in a joint doctoral program in computational science with San Diego State University and the University of California at Irvine, Irvine, CA, USA. His research interests include wireless communications, signal processing, and machine learning. He received a Best Paper Award from the 2020 IEEE International Conference on Communications (ICC).
Duy Nguyen and Lee Swindlehurst
Low-resolution analog-to-digital converters (ADCs) have been considered as a practical and promising solution for reducing cost and power consumption in massive Multiple-Input Multiple-Output (MIMO) systems. Unfortunately, low-resolution ADCs significantly distort the received signals, and thus make data detection much more challenging. We develop a new deep neural network (DNN) framework for efficient and low-complexity data detection in low-resolution massive MIMO systems. Based on a reformulated maximum likelihood detection problem, we propose a model-driven DNN-based detector, namely FBMNet, for few-bit massive MIMO systems. The proposed FBMNet detector has a unique and simple structure designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented. Numerical results also show that FBMNet significantly outperforms existing detection methods.
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