aalto1 untyped-item.component.html
AdaBoost-Based Efficient Channel Estimation and Data Detection in One-Bit Massive MIMO
Loading...
Access rights
openAccess
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Date
2024
Department
Department of Information and Communications Engineering
Major/Subject
Mcode
Degree programme
Language
en
Pages
11
Series
IEEE Transactions on Wireless Communications, Volume 23, issue 10, pp. 13935-13945
Abstract
The use of one-bit analog-to-digital converter (ADC) has been considered as a viable alternative to high resolution counterparts in realizing and commercializing massive multiple-input multiple-output (MIMO) systems. However, the issue of discarding the amplitude information by one-bit quantizers has to be compensated. Thus, carefully tailored methods need to be developed for one-bit channel estimation and data detection as the conventional ones cannot be used. To address these issues, the problems of one-bit channel estimation and data detection for MIMO orthogonal frequency division multiplexing (OFDM) system that operates over uncorrelated frequency selective channels are investigated here. We first develop channel estimators that exploit Gaussian discriminant analysis (GDA) classifier and approximate versions of it as the so-called weak classifiers in an adaptive boosting (AdaBoost) approach. Particularly, the combination of the approximate GDA classifiers with AdaBoost offers the benefit of scalability with the linear order of computations, which is critical in massive MIMO-OFDM systems. We then take advantage of the same idea for proposing the data detectors. Numerical results validate the efficiency of the proposed channel estimators and data detectors compared to other methods. They show comparable/better performance to that of the state-of-the-art methods, but require dramatically lower computational complexities and run times.
Description
Publisher Copyright: Authors
Keywords
AdaBoost, channel estimation, Channel estimation, Computational complexity, data detection, Detectors, frequency selective channel, Massive MIMO, massive MIMO-OFDM, OFDM, One-bit ADC, Training, Vectors
Other note
Citation
Esfandiari, M, Vorobyov, S A & Heath, R W 2024, 'AdaBoost-Based Efficient Channel Estimation and Data Detection in One-Bit Massive MIMO', IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 13935-13945. https://doi.org/10.1109/TWC.2024.3406782