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MIMO Detection under Hardware Impairments: Learning with Noisy Labels SCIE SCOPUS

Title
MIMO Detection under Hardware Impairments: Learning with Noisy Labels
Authors
Kwon, JinmanSEUNGHYUN, JEONJeon, Yo-SebPoor, H. Vincent
Date Issued
2024-06
Publisher
Institute of Electrical and Electronics Engineers
Abstract
This paper considers a data detection problem in multiple-input multiple-output (MIMO) communication systems with hardware impairments. To address challenges posed by nonlinear and unknown distortion in received signals, two learning-based detection methods, referred to as model-driven and data-driven, are presented. The model-driven method employs a generalized Gaussian distortion model to approximate the conditional distribution of the distorted received signal. By using the outputs of coarse data detection as noisy training data, the model-driven method avoids the need for additional signaling overhead beyond traditional pilot overhead for channel estimation. An expectation-maximization algorithm is devised to accurately learn the parameters of the distortion model from noisy training data. To resolve a model mismatch problem in the model-driven method, the data-driven method employs a deep neural network (DNN) for approximating a-posteriori probabilities for each received signal. This method uses the outputs of the model-driven method as noisy labels and therefore does not require extra training overhead. To avoid the overfitting problem caused by noisy labels, a robust DNN training algorithm is devised, which involves a warm-up period, sample selection, and loss correction. Simulation results demonstrate that the two proposed methods outperform existing solutions with the same overhead under various hardware impairment scenarios. IEEE
URI
https://oasis.postech.ac.kr/handle/2014.oak/120324
DOI
10.1109/twc.2023.3329521
ISSN
1536-1276
Article Type
Article
Citation
IEEE Transactions on Wireless Communications, vol. 23, no. 6, page. 1 - 1, 2024-06
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전요셉JEON, YO SEB
Dept of Electrical Enginrg
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