Novel Wavelet Convolutional Neural Networks for Signal Detection in OFDM-IM Systems
Orthogonal Frequency Division Multiplexing with Index Modulation (OFDM-IM) is regarded as a promising candidate for next generation communications due to its remarkable efficiency and flexibility. In the field of wireless communications, deep learning, particularly Convolutional Neural Networks (CNN...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/11107403/ |
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| author | Yang Zhao SI-YU Zhang Yuexia Zhang Gongpu Wang Behnam Shahrrava |
| author_facet | Yang Zhao SI-YU Zhang Yuexia Zhang Gongpu Wang Behnam Shahrrava |
| author_sort | Yang Zhao |
| collection | DOAJ |
| description | Orthogonal Frequency Division Multiplexing with Index Modulation (OFDM-IM) is regarded as a promising candidate for next generation communications due to its remarkable efficiency and flexibility. In the field of wireless communications, deep learning, particularly Convolutional Neural Networks (CNNs), has been extensively utilized for tasks such as channel estimation and signal detection. However, CNNs' limited receptive field growth poses a challenge in capturing long range dependencies. To achieve efficient deep learning based OFDM-IM detection, this paper proposes two novel OFDM-IM signal detection networks that integrate wavelet transforms with CNNs (WTConv). The first proposed network, referred to as Dual Stage Wavelet Convolutions (DS-WTConv), adopts a dual stage architecture. It comprises an Index Feature Extraction Sub-Network (IdxNet) and a Signal Feature Reconstruction Sub-Network (DetNet). The second network, named Single Network Wavelet Convolutions (SN-WTConv), features a more compact single stage design that combines wavelet convolution and CNN layers. Extensive simulation results demonstrate that both the DS-WTConv and SN-WTConv networks exhibit superior bit error rate (BER) performance and lower computational complexity compared to existing conventional and deep learning-based approaches. Compared to the existing deep learning based detection schemes, the proposed WTConv-based networks reduce the BER by up to 35.3%, and the running time by up to 30.1%. Compared to the optimal Maximum likelihood (ML) method, the proposed DS-WTConv and SN-WTConv achieve approximately 19.2 times and 11.3 times faster runtime, respectively. |
| format | Article |
| id | doaj-art-8a6118e006c14508a8fb73af2ed51a2b |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-8a6118e006c14508a8fb73af2ed51a2b2025-08-25T23:19:06ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302025-01-0162210222310.1109/OJVT.2025.359520011107403Novel Wavelet Convolutional Neural Networks for Signal Detection in OFDM-IM SystemsYang Zhao0SI-YU Zhang1https://orcid.org/0000-0002-3531-9228Yuexia Zhang2https://orcid.org/0000-0003-3546-473XGongpu Wang3https://orcid.org/0000-0001-5346-3162Behnam Shahrrava4https://orcid.org/0000-0001-6457-4167School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing, ChinaSchool of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing, ChinaSchool of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing, ChinaBeijing Key Lab of Transportation Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, ChinaDepartment of Electrical and Computer Engineering, University of Windsor, Ontario, ON, CanadaOrthogonal Frequency Division Multiplexing with Index Modulation (OFDM-IM) is regarded as a promising candidate for next generation communications due to its remarkable efficiency and flexibility. In the field of wireless communications, deep learning, particularly Convolutional Neural Networks (CNNs), has been extensively utilized for tasks such as channel estimation and signal detection. However, CNNs' limited receptive field growth poses a challenge in capturing long range dependencies. To achieve efficient deep learning based OFDM-IM detection, this paper proposes two novel OFDM-IM signal detection networks that integrate wavelet transforms with CNNs (WTConv). The first proposed network, referred to as Dual Stage Wavelet Convolutions (DS-WTConv), adopts a dual stage architecture. It comprises an Index Feature Extraction Sub-Network (IdxNet) and a Signal Feature Reconstruction Sub-Network (DetNet). The second network, named Single Network Wavelet Convolutions (SN-WTConv), features a more compact single stage design that combines wavelet convolution and CNN layers. Extensive simulation results demonstrate that both the DS-WTConv and SN-WTConv networks exhibit superior bit error rate (BER) performance and lower computational complexity compared to existing conventional and deep learning-based approaches. Compared to the existing deep learning based detection schemes, the proposed WTConv-based networks reduce the BER by up to 35.3%, and the running time by up to 30.1%. Compared to the optimal Maximum likelihood (ML) method, the proposed DS-WTConv and SN-WTConv achieve approximately 19.2 times and 11.3 times faster runtime, respectively.https://ieeexplore.ieee.org/document/11107403/Deep learningwavelet transformconvolutional neural networkwavelet convolutionindex modulationorthogonal frequency division multiplexing |
| spellingShingle | Yang Zhao SI-YU Zhang Yuexia Zhang Gongpu Wang Behnam Shahrrava Novel Wavelet Convolutional Neural Networks for Signal Detection in OFDM-IM Systems IEEE Open Journal of Vehicular Technology Deep learning wavelet transform convolutional neural network wavelet convolution index modulation orthogonal frequency division multiplexing |
| title | Novel Wavelet Convolutional Neural Networks for Signal Detection in OFDM-IM Systems |
| title_full | Novel Wavelet Convolutional Neural Networks for Signal Detection in OFDM-IM Systems |
| title_fullStr | Novel Wavelet Convolutional Neural Networks for Signal Detection in OFDM-IM Systems |
| title_full_unstemmed | Novel Wavelet Convolutional Neural Networks for Signal Detection in OFDM-IM Systems |
| title_short | Novel Wavelet Convolutional Neural Networks for Signal Detection in OFDM-IM Systems |
| title_sort | novel wavelet convolutional neural networks for signal detection in ofdm im systems |
| topic | Deep learning wavelet transform convolutional neural network wavelet convolution index modulation orthogonal frequency division multiplexing |
| url | https://ieeexplore.ieee.org/document/11107403/ |
| work_keys_str_mv | AT yangzhao novelwaveletconvolutionalneuralnetworksforsignaldetectioninofdmimsystems AT siyuzhang novelwaveletconvolutionalneuralnetworksforsignaldetectioninofdmimsystems AT yuexiazhang novelwaveletconvolutionalneuralnetworksforsignaldetectioninofdmimsystems AT gongpuwang novelwaveletconvolutionalneuralnetworksforsignaldetectioninofdmimsystems AT behnamshahrrava novelwaveletconvolutionalneuralnetworksforsignaldetectioninofdmimsystems |