Deep Learning Based Signal Detection in Dual Mode Generalized Spatial Modulation

Kaiyue Yang; Zhiquan Bai; Jinmei Zhang; Ke Pang; Xinhong Hao

doi:10.1109/ICUFN55119.2022.9829706

Deep Learning Based Signal Detection in Dual Mode Generalized Spatial Modulation

Kaiyue Yang, Zhiquan Bai^*, Jinmei Zhang, Ke Pang, Xinhong Hao

^*Corresponding author for this work

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

Abstract

This paper proposes a kind of deep learning (DL) based signal detection in dual mode generalized spatial modulation (DM-GSM) system, which aims to balance the detection performance and the complexity. Specifically, two neural networks, deep neural network (DNN) and convolutional neural network (CNN), are utilized to gain the mapping relationship among the received symbols, the channel matrix, and the transmitted bits. After offline training, the trained network is deployed for the online signal detection according to the input feature vector. Simulation results illustrate that the proposed DL detection can obtain the approximate performance of maximum likelihood (ML) detection at lower complexity and can provide better robustness compared with the conventional detection algorithms in the presence of various noise deviating from the standard Gaussian distribution.

Original language	English
Title of host publication	ICUFN 2022 - 13th International Conference on Ubiquitous and Future Networks
Publisher	IEEE Computer Society
Pages	140-144
Number of pages	5
ISBN (Electronic)	9781665485500
DOIs	https://doi.org/10.1109/ICUFN55119.2022.9829706
Publication status	Published - 2022
Externally published	Yes
Event	13th International Conference on Ubiquitous and Future Networks, ICUFN 2022 - Virtual, Barcelona, Spain Duration: 5 Jul 2022 → 8 Jul 2022

Publication series

Name	International Conference on Ubiquitous and Future Networks, ICUFN
Volume	2022-July
ISSN (Print)	2165-8528
ISSN (Electronic)	2165-8536

Conference

Conference	13th International Conference on Ubiquitous and Future Networks, ICUFN 2022
Country/Territory	Spain
City	Virtual, Barcelona
Period	5/07/22 → 8/07/22

Keywords

Dual mode generalized spatial modulation (DMGSM)
convolutional neural network (CNN)
deep neural network (DNN)
signal detection

Access to Document

10.1109/ICUFN55119.2022.9829706

Cite this

Yang, K., Bai, Z., Zhang, J., Pang, K., & Hao, X. (2022). Deep Learning Based Signal Detection in Dual Mode Generalized Spatial Modulation. In ICUFN 2022 - 13th International Conference on Ubiquitous and Future Networks (pp. 140-144). (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2022-July). IEEE Computer Society. https://doi.org/10.1109/ICUFN55119.2022.9829706

@inproceedings{80bf82eaa5bb427a9c41325475edbed8,

title = "Deep Learning Based Signal Detection in Dual Mode Generalized Spatial Modulation",

abstract = "This paper proposes a kind of deep learning (DL) based signal detection in dual mode generalized spatial modulation (DM-GSM) system, which aims to balance the detection performance and the complexity. Specifically, two neural networks, deep neural network (DNN) and convolutional neural network (CNN), are utilized to gain the mapping relationship among the received symbols, the channel matrix, and the transmitted bits. After offline training, the trained network is deployed for the online signal detection according to the input feature vector. Simulation results illustrate that the proposed DL detection can obtain the approximate performance of maximum likelihood (ML) detection at lower complexity and can provide better robustness compared with the conventional detection algorithms in the presence of various noise deviating from the standard Gaussian distribution.",

keywords = "Dual mode generalized spatial modulation (DMGSM), convolutional neural network (CNN), deep neural network (DNN), signal detection",

author = "Kaiyue Yang and Zhiquan Bai and Jinmei Zhang and Ke Pang and Xinhong Hao",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Ubiquitous and Future Networks, ICUFN 2022 ; Conference date: 05-07-2022 Through 08-07-2022",

year = "2022",

doi = "10.1109/ICUFN55119.2022.9829706",

language = "English",

series = "International Conference on Ubiquitous and Future Networks, ICUFN",

publisher = "IEEE Computer Society",

pages = "140--144",

booktitle = "ICUFN 2022 - 13th International Conference on Ubiquitous and Future Networks",

address = "United States",

}

Yang, K, Bai, Z, Zhang, J, Pang, K & Hao, X 2022, Deep Learning Based Signal Detection in Dual Mode Generalized Spatial Modulation. in ICUFN 2022 - 13th International Conference on Ubiquitous and Future Networks. International Conference on Ubiquitous and Future Networks, ICUFN, vol. 2022-July, IEEE Computer Society, pp. 140-144, 13th International Conference on Ubiquitous and Future Networks, ICUFN 2022, Virtual, Barcelona, Spain, 5/07/22. https://doi.org/10.1109/ICUFN55119.2022.9829706

Deep Learning Based Signal Detection in Dual Mode Generalized Spatial Modulation. / Yang, Kaiyue; Bai, Zhiquan; Zhang, Jinmei et al.
ICUFN 2022 - 13th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2022. p. 140-144 (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2022-July).

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

TY - GEN

T1 - Deep Learning Based Signal Detection in Dual Mode Generalized Spatial Modulation

AU - Yang, Kaiyue

AU - Bai, Zhiquan

AU - Zhang, Jinmei

AU - Pang, Ke

AU - Hao, Xinhong

PY - 2022

Y1 - 2022

N2 - This paper proposes a kind of deep learning (DL) based signal detection in dual mode generalized spatial modulation (DM-GSM) system, which aims to balance the detection performance and the complexity. Specifically, two neural networks, deep neural network (DNN) and convolutional neural network (CNN), are utilized to gain the mapping relationship among the received symbols, the channel matrix, and the transmitted bits. After offline training, the trained network is deployed for the online signal detection according to the input feature vector. Simulation results illustrate that the proposed DL detection can obtain the approximate performance of maximum likelihood (ML) detection at lower complexity and can provide better robustness compared with the conventional detection algorithms in the presence of various noise deviating from the standard Gaussian distribution.

AB - This paper proposes a kind of deep learning (DL) based signal detection in dual mode generalized spatial modulation (DM-GSM) system, which aims to balance the detection performance and the complexity. Specifically, two neural networks, deep neural network (DNN) and convolutional neural network (CNN), are utilized to gain the mapping relationship among the received symbols, the channel matrix, and the transmitted bits. After offline training, the trained network is deployed for the online signal detection according to the input feature vector. Simulation results illustrate that the proposed DL detection can obtain the approximate performance of maximum likelihood (ML) detection at lower complexity and can provide better robustness compared with the conventional detection algorithms in the presence of various noise deviating from the standard Gaussian distribution.

KW - Dual mode generalized spatial modulation (DMGSM)

KW - convolutional neural network (CNN)

KW - deep neural network (DNN)

KW - signal detection

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DO - 10.1109/ICUFN55119.2022.9829706

M3 - Conference contribution

AN - SCOPUS:85135220423

T3 - International Conference on Ubiquitous and Future Networks, ICUFN

SP - 140

EP - 144

BT - ICUFN 2022 - 13th International Conference on Ubiquitous and Future Networks

PB - IEEE Computer Society

T2 - 13th International Conference on Ubiquitous and Future Networks, ICUFN 2022

Y2 - 5 July 2022 through 8 July 2022

ER -

Deep Learning Based Signal Detection in Dual Mode Generalized Spatial Modulation

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