Smart Detection Using the Cascaded Artificial Neural Network for OFDM with Subcarrier Number Modulation

Jun Li; Shuping Dang; Miaowen Wen; Zhenrong Zhang; Qiang Li

doi:10.1109/LWC.2021.3062686

Smart Detection Using the Cascaded Artificial Neural Network for OFDM with Subcarrier Number Modulation

Jun Li, Shuping Dang, Miaowen Wen, Zhenrong Zhang, Qiang Li

Computer, Electrical and Mathematical Sciences and Engineering

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

Since the proposal of orthogonal frequency-division multiplexing with subcarrier number modulation (OFDM-SNM), the high detection complexity of this novel modulation scheme becomes a major issue degrading its applicability in practice. To provide an easy-to-implement and low-complexity solution to the detection of the OFDM-SNM signal, we design a cascaded artificial neural network (CANN) at the OFDM-SNM receiver to decouple the detection of subcarrier number pattern (SNP) and constellation symbols on the active subcarriers. This is the first time that the CANN design is introduced to assist in detecting parallel signals. Numerical simulations verify the effectiveness and efficiency of the proposed smart detection scheme and show that the CANN based smart detection with adequate training can yield comparable error performance to the optimal maximum-likelihood detection at much lower complexity.

Original language	English (US)
Journal	IEEE Wireless Communications Letters
DOIs	https://doi.org/10.1109/LWC.2021.3062686
State	Published - 2021

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10.1109/LWC.2021.3062686

https://repository.kaust.edu.sa/bitstream/10754/667806/1/Double%20%281%29.pdf

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@article{8654a7541f9641de9d84f17cbb305ae0,

title = "Smart Detection Using the Cascaded Artificial Neural Network for OFDM with Subcarrier Number Modulation",

abstract = "Since the proposal of orthogonal frequency-division multiplexing with subcarrier number modulation (OFDM-SNM), the high detection complexity of this novel modulation scheme becomes a major issue degrading its applicability in practice. To provide an easy-to-implement and low-complexity solution to the detection of the OFDM-SNM signal, we design a cascaded artificial neural network (CANN) at the OFDM-SNM receiver to decouple the detection of subcarrier number pattern (SNP) and constellation symbols on the active subcarriers. This is the first time that the CANN design is introduced to assist in detecting parallel signals. Numerical simulations verify the effectiveness and efficiency of the proposed smart detection scheme and show that the CANN based smart detection with adequate training can yield comparable error performance to the optimal maximum-likelihood detection at much lower complexity.",

author = "Jun Li and Shuping Dang and Miaowen Wen and Zhenrong Zhang and Qiang Li",

note = "KAUST Repository Item: Exported on 2021-03-03 Acknowledgements: This work was supported in part by National Natural Science Foundation of China under Grants 61872102 and 61661004, in part by the International Collaborative Research Program of Guangdong Science and Technology Department under Grant 2020A0505100061, in part by the Pearl River Nova Program of Guangzhou under Grant 201806010171, in part by the Fundamental Research Funds for the Central Universities under Grant 2019SJ02, and in part by the Guangxi Science Key Research and Development Project under Grant AB1850043.",

year = "2021",

doi = "10.1109/LWC.2021.3062686",

language = "English (US)",

journal = "IEEE Wireless Communications Letters",

issn = "2162-2345",

publisher = "IEEE Communications Society",

}

TY - JOUR

T1 - Smart Detection Using the Cascaded Artificial Neural Network for OFDM with Subcarrier Number Modulation

AU - Li, Jun

AU - Dang, Shuping

AU - Wen, Miaowen

AU - Zhang, Zhenrong

AU - Li, Qiang

N1 - KAUST Repository Item: Exported on 2021-03-03 Acknowledgements: This work was supported in part by National Natural Science Foundation of China under Grants 61872102 and 61661004, in part by the International Collaborative Research Program of Guangdong Science and Technology Department under Grant 2020A0505100061, in part by the Pearl River Nova Program of Guangzhou under Grant 201806010171, in part by the Fundamental Research Funds for the Central Universities under Grant 2019SJ02, and in part by the Guangxi Science Key Research and Development Project under Grant AB1850043.

PY - 2021

Y1 - 2021

N2 - Since the proposal of orthogonal frequency-division multiplexing with subcarrier number modulation (OFDM-SNM), the high detection complexity of this novel modulation scheme becomes a major issue degrading its applicability in practice. To provide an easy-to-implement and low-complexity solution to the detection of the OFDM-SNM signal, we design a cascaded artificial neural network (CANN) at the OFDM-SNM receiver to decouple the detection of subcarrier number pattern (SNP) and constellation symbols on the active subcarriers. This is the first time that the CANN design is introduced to assist in detecting parallel signals. Numerical simulations verify the effectiveness and efficiency of the proposed smart detection scheme and show that the CANN based smart detection with adequate training can yield comparable error performance to the optimal maximum-likelihood detection at much lower complexity.

AB - Since the proposal of orthogonal frequency-division multiplexing with subcarrier number modulation (OFDM-SNM), the high detection complexity of this novel modulation scheme becomes a major issue degrading its applicability in practice. To provide an easy-to-implement and low-complexity solution to the detection of the OFDM-SNM signal, we design a cascaded artificial neural network (CANN) at the OFDM-SNM receiver to decouple the detection of subcarrier number pattern (SNP) and constellation symbols on the active subcarriers. This is the first time that the CANN design is introduced to assist in detecting parallel signals. Numerical simulations verify the effectiveness and efficiency of the proposed smart detection scheme and show that the CANN based smart detection with adequate training can yield comparable error performance to the optimal maximum-likelihood detection at much lower complexity.

UR - http://hdl.handle.net/10754/667806

UR - https://ieeexplore.ieee.org/document/9365714/

U2 - 10.1109/LWC.2021.3062686

DO - 10.1109/LWC.2021.3062686

M3 - Article

SN - 2162-2345

JO - IEEE Wireless Communications Letters

JF - IEEE Wireless Communications Letters

ER -

Smart Detection Using the Cascaded Artificial Neural Network for OFDM with Subcarrier Number Modulation

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