An Efficient Deep Network for Modulation Classification in Impaired MIMO-OFDM Systems

Thien Huynh-The; Quoc Viet Pham; Toan Van Nguyen; Daniel Benevides Da Costa; Van Phuc Hoang

doi:10.1109/SSP53291.2023.10208038

An Efficient Deep Network for Modulation Classification in Impaired MIMO-OFDM Systems

Thien Huynh-The^*, Quoc Viet Pham, Toan Van Nguyen, Daniel Benevides Da Costa, Van Phuc Hoang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In this paper, an efficient automatic modulation classification for MIMO-OFDM signals is proposed for next generation wireless networks by exploiting cutting-edge deep learning techniques. Particularly, we design a deep network, namely OFDM modulation classification network (OMCNet), with asymmetric depthwise separable convolution, residual connection, and attention mechanism in a sophisticated design of processing blocks to reduce the overall complexity without sacrificing learning efficiency. Relying on the simulations, our deep network achieves over 92% for different delay spread models demonstrates the robustness of modulation classification under various channel impairments. Remarkably, compared with a baseline model, OMCNet reduces the network size by four times and computation cost by two times with the asymmetric depthwise separable while achieving a competitive accuracy thanks to residual connection and attention mechanism.

Original language	English
Title of host publication	Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023
Publisher	IEEE Computer Society
Pages	130-134
Number of pages	5
ISBN (Electronic)	9781665452458
DOIs	https://doi.org/10.1109/SSP53291.2023.10208038
State	Published - 2023
Externally published	Yes
Event	22nd IEEE Statistical Signal Processing Workshop, SSP 2023 - Hanoi, Viet Nam Duration: 2 Jul 2023 → 5 Jul 2023

Publication series

Name	IEEE Workshop on Statistical Signal Processing Proceedings
Volume	2023-July

Conference

Conference	22nd IEEE Statistical Signal Processing Workshop, SSP 2023
Country/Territory	Viet Nam
City	Hanoi
Period	2/07/23 → 5/07/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Cognitive radio
MIMO-OFDM systems
convolutional neural networks
deep learning
modulation identification

ASJC Scopus subject areas

Electrical and Electronic Engineering
Applied Mathematics
Signal Processing
Computer Science Applications

Access to Document

10.1109/SSP53291.2023.10208038

Cite this

Huynh-The, T., Pham, Q. V., Nguyen, T. V., Da Costa, D. B., & Hoang, V. P. (2023). An Efficient Deep Network for Modulation Classification in Impaired MIMO-OFDM Systems. In Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023 (pp. 130-134). (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2023-July). IEEE Computer Society. https://doi.org/10.1109/SSP53291.2023.10208038

@inproceedings{cd181c99cfb94e58b9e1dd9aeea26c8d,

title = "An Efficient Deep Network for Modulation Classification in Impaired MIMO-OFDM Systems",

abstract = "In this paper, an efficient automatic modulation classification for MIMO-OFDM signals is proposed for next generation wireless networks by exploiting cutting-edge deep learning techniques. Particularly, we design a deep network, namely OFDM modulation classification network (OMCNet), with asymmetric depthwise separable convolution, residual connection, and attention mechanism in a sophisticated design of processing blocks to reduce the overall complexity without sacrificing learning efficiency. Relying on the simulations, our deep network achieves over 92\% for different delay spread models demonstrates the robustness of modulation classification under various channel impairments. Remarkably, compared with a baseline model, OMCNet reduces the network size by four times and computation cost by two times with the asymmetric depthwise separable while achieving a competitive accuracy thanks to residual connection and attention mechanism.",

keywords = "Cognitive radio, MIMO-OFDM systems, convolutional neural networks, deep learning, modulation identification",

author = "Thien Huynh-The and Pham, \{Quoc Viet\} and Nguyen, \{Toan Van\} and \{Da Costa\}, \{Daniel Benevides\} and Hoang, \{Van Phuc\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 22nd IEEE Statistical Signal Processing Workshop, SSP 2023 ; Conference date: 02-07-2023 Through 05-07-2023",

year = "2023",

doi = "10.1109/SSP53291.2023.10208038",

language = "English",

series = "IEEE Workshop on Statistical Signal Processing Proceedings",

publisher = "IEEE Computer Society",

pages = "130--134",

booktitle = "Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023",

address = "United States",

}

Huynh-The, T, Pham, QV, Nguyen, TV, Da Costa, DB & Hoang, VP 2023, An Efficient Deep Network for Modulation Classification in Impaired MIMO-OFDM Systems. in Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023. IEEE Workshop on Statistical Signal Processing Proceedings, vol. 2023-July, IEEE Computer Society, pp. 130-134, 22nd IEEE Statistical Signal Processing Workshop, SSP 2023, Hanoi, Viet Nam, 2/07/23. https://doi.org/10.1109/SSP53291.2023.10208038

An Efficient Deep Network for Modulation Classification in Impaired MIMO-OFDM Systems. / Huynh-The, Thien; Pham, Quoc Viet; Nguyen, Toan Van et al.
Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023. IEEE Computer Society, 2023. p. 130-134 (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2023-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Huynh-The, Thien

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AU - Hoang, Van Phuc

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AB - In this paper, an efficient automatic modulation classification for MIMO-OFDM signals is proposed for next generation wireless networks by exploiting cutting-edge deep learning techniques. Particularly, we design a deep network, namely OFDM modulation classification network (OMCNet), with asymmetric depthwise separable convolution, residual connection, and attention mechanism in a sophisticated design of processing blocks to reduce the overall complexity without sacrificing learning efficiency. Relying on the simulations, our deep network achieves over 92% for different delay spread models demonstrates the robustness of modulation classification under various channel impairments. Remarkably, compared with a baseline model, OMCNet reduces the network size by four times and computation cost by two times with the asymmetric depthwise separable while achieving a competitive accuracy thanks to residual connection and attention mechanism.

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An Efficient Deep Network for Modulation Classification in Impaired MIMO-OFDM Systems

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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Cite this