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Hybrid Spectrum Sensing Using Neural Network–Based MF and ED for Enhanced Detection in Rayleigh Channel.

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Title: Hybrid Spectrum Sensing Using Neural Network–Based MF and ED for Enhanced Detection in Rayleigh Channel.
Authors: Kumar, Arun1 (AUTHOR), Gaur, Nishant2 (AUTHOR), Nanthaamornphong, Aziz3 (AUTHOR) aziz.n@phuket.psu.ac.th, Tani, Andrea (AUTHOR)
Source: Journal of Electrical & Computer Engineering. 3/3/2025, Vol. 2025, p1-15. 15p.
Subjects: Bit error rate, Dynamic spectrum access, Wireless communications, Matched filters, Radio technology, Cognitive radio
Abstract: Spectrum sensing (SS) is an integral part of cognitive radio systems, allowing for dynamic spectrum access and efficient exploitation of scarce spectral resources. Classic spectrum sensing methods, such as matched filters (MFs) and energy detections (EDs), usually fail in low‐SNR and interference‐rich scenarios, with poor detection performance and suboptimal spectrum usage. This work proposes a hybrid spectrum sensing approach that combines the neural network (NN)‐based MF and ED to address these limitations. The NNs act as an intelligent signal processor that uses its ability to learn and adapt to different channel conditions to enhance signal detection in low‐SNR environments. The proposed framework combines the accuracy of MF with the adaptability of ED, guided by a NN to improve decision‐making accuracy. Extensive simulations demonstrate that the method achieves significant improvements in detection accuracy, false alarm reduction, and spectrum hole identification compared to traditional approaches. Furthermore, the capability of the NN to mitigate noise and interference results in enhanced bit error rate (BER) performance, ensuring reliable communication. The paper assesses the system performance in terms of the key metrics, BER, probability of detection (Pd), and probability of false alarm (Pfa), power spectral density (PSD), and capacity, thereby indicating robustness toward dynamic and noisy environments. The results, therefore, open up a potential for defining spectrum sensing by NNs as a scalable, adaptive, and efficient solution for future wireless communication systems in applications such as IoT, 5G, and next‐generation cognitive radios. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Electrical & Computer Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Hybrid Spectrum Sensing Using Neural Network–Based MF and ED for Enhanced Detection in Rayleigh Channel.
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  Data: <searchLink fieldCode="AR" term="%22Kumar%2C+Arun%22">Kumar, Arun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gaur%2C+Nishant%22">Gaur, Nishant</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nanthaamornphong%2C+Aziz%22">Nanthaamornphong, Aziz</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> aziz.n@phuket.psu.ac.th</i><br /><searchLink fieldCode="AR" term="%22Tani%2C+Andrea%22">Tani, Andrea</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Electrical+%26+Computer+Engineering%22">Journal of Electrical & Computer Engineering</searchLink>. 3/3/2025, Vol. 2025, p1-15. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Bit+error+rate%22">Bit error rate</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+spectrum+access%22">Dynamic spectrum access</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+communications%22">Wireless communications</searchLink><br /><searchLink fieldCode="DE" term="%22Matched+filters%22">Matched filters</searchLink><br /><searchLink fieldCode="DE" term="%22Radio+technology%22">Radio technology</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+radio%22">Cognitive radio</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Spectrum sensing (SS) is an integral part of cognitive radio systems, allowing for dynamic spectrum access and efficient exploitation of scarce spectral resources. Classic spectrum sensing methods, such as matched filters (MFs) and energy detections (EDs), usually fail in low‐SNR and interference‐rich scenarios, with poor detection performance and suboptimal spectrum usage. This work proposes a hybrid spectrum sensing approach that combines the neural network (NN)‐based MF and ED to address these limitations. The NNs act as an intelligent signal processor that uses its ability to learn and adapt to different channel conditions to enhance signal detection in low‐SNR environments. The proposed framework combines the accuracy of MF with the adaptability of ED, guided by a NN to improve decision‐making accuracy. Extensive simulations demonstrate that the method achieves significant improvements in detection accuracy, false alarm reduction, and spectrum hole identification compared to traditional approaches. Furthermore, the capability of the NN to mitigate noise and interference results in enhanced bit error rate (BER) performance, ensuring reliable communication. The paper assesses the system performance in terms of the key metrics, BER, probability of detection (Pd), and probability of false alarm (Pfa), power spectral density (PSD), and capacity, thereby indicating robustness toward dynamic and noisy environments. The results, therefore, open up a potential for defining spectrum sensing by NNs as a scalable, adaptive, and efficient solution for future wireless communication systems in applications such as IoT, 5G, and next‐generation cognitive radios. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Electrical & Computer Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1155/jece/9506922
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        Text: English
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        Type: general
      – SubjectFull: Dynamic spectrum access
        Type: general
      – SubjectFull: Wireless communications
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      – SubjectFull: Matched filters
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      – SubjectFull: Radio technology
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      – SubjectFull: Cognitive radio
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      – TitleFull: Hybrid Spectrum Sensing Using Neural Network–Based MF and ED for Enhanced Detection in Rayleigh Channel.
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            NameFull: Kumar, Arun
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            NameFull: Nanthaamornphong, Aziz
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            – D: 03
              M: 03
              Text: 3/3/2025
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              Y: 2025
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