Intelligent spectrum sensing algorithm for cognitive internet of vehicles based on KPCA and improved CNN

Duan, Yanyan; Huang, Fenghua; Xu, Lingwei; Gulliver, T. Aaron

doi:10.1007/s12083-023-01501-0

Cited by 3 publications

(3 citation statements)

References 32 publications

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“…Based on deep learning [53][54][55][56][57][58][59][60][61] CNN By extracting higher-order statistical features of the signal, it is possible to achieve higher detection probabilities at low SNRs, and the model is also more robust.…”

Section: Performance Comparison Of Conventional Methods and Deep-lear...mentioning

confidence: 99%

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A Review of Research on Spectrum Sensing Based on Deep Learning

Zhang,

Luo

2023

Electronics

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In recent years, with the rapid development in wireless communication and 5G networks, the rapid growth in mobile users has been accompanied by an increasing demand for the electromagnetic spectrum. The birth of cognitive radio and its spectrum-sensing technology provides hope for solving the problem of low utilization of the wireless spectrum. Artificial intelligence (AI) has been widely discussed globally. Deep learning technology, known for its strong learning ability and adaptability, plays a significant role in this field. Moreover, integrating deep learning with wireless communication technology has become a prominent research direction in recent years. The research objective of this paper is to summarize the algorithm of cognitive radio spectrum-sensing technology combined with deep learning technology. To review the advantages of deep-learning-based spectrum-sensing algorithms, this paper first introduces the traditional spectrum-sensing methods. It summarizes and compares the advantages and disadvantages of each method. It then describes the application of deep learning algorithms in spectrum sensing and focuses on the typical deep-neural-network-based sensing methods. Then, the existing deep-learning-based cooperative spectrum-sensing methods are summarized. Finally, the deep learning spectrum-sensing methods are discussed, along with challenges in the field and future research directions.

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Section: Performance Comparison Of Conventional Methods and Deep-lear...mentioning

confidence: 99%

“…Moreover, Yanyan Duan et al utilize the method in their 2023 article [59]. Their main focus is on the application of deep learning for spectrum sensing in connected vehicle communications.…”

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confidence: 99%

A Review of Research on Spectrum Sensing Based on Deep Learning

Zhang,

Luo

2023

Electronics

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“…are called polynomial kernel function, sigmoid kernel function and Gaussian kernel function, respectively[31][32][33][34]. The parameters d > 0 , 0 1 β > 0, β < 0 and c can be adjusted arbitrarily.…”

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confidence: 99%

Fault detection and separation of hybrid electric vehicles based on kernel orthogonal subspace analysis

Wang,

Deprizon,

Peng

et al. 2023

J Appl Eng Science

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Driving quality and vehicles safety of hybrid electric vehicles (HEVs) are two hot-topic issues in automobile technology. Nowadays, research focuses to more intelligent and convenient HEVs fault detection methods. This paper will focus on the fault detection of HEV powertrain system with a data-driven algorithm. Orthonormal subspace analysis (OSA) is a newly proposed data-driven method which adds the ability of fault separation. Nonetheless, the linear OSA algorithm cannot effectively detect powertrain system faults, since these faults present complex nonlinear characteristics. A new kernel OSA (KOSA) method is proposed to transform the nonlinear problem into a linear problem through the mapping of kernel function and the dimensionality reduction technique of OSA. Testing results on a nonlinear model and real samples of XMQ6127AGCHEVN61 HEV show that KOSA address the nonlinear problems and it performs better than OSA and kernel principal component analysis (KPCA)

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Cross-attention mechanism-based spectrum sensing in generalized Gaussian noise

Xi,

Guo,

Yang

et al. 2024

Sci Rep

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Spectrum sensing (SS) technology is essential for cognitive radio (CR) networks to effectively identify and utilize idle spectrum resources. Due to the influence of noise characteristics in the channel, providing accurate sensing results is challenging. In order to improve the performance of SS under non-Gaussian noise and overcome the limitations of existing methods that are mostly based on a single feature, we propose a novel time-frequency cross fusion network (TFCFN). Specifically, we utilize gated recurrent units (GRU) to capture long-term dependencies in the time domain on the original signals, meanwhile, we perform a fast Fourier transform (FFT) on the original signals to obtain the frequency domain information, and subsequently use convolutional neural networks (CNN) to extract the local spatial features in the frequency domain. Ultimately, these time-domain and frequency-domain features are dynamically fused through a cross-attention mechanism to construct more comprehensive and robust features for signal classification. We use generalized Gaussian distribution (GGD) as the noise model and reconstruct the RadioML2016.10a dataset to explore the performance under various noise conditions. The experimental results show that compared with the baseline methods, TFCFN exhibits better detection ability and maintains lower complexity in both Gaussian and non-Gaussian noise environments. Notably, when the shape parameter of GGD is set to 0.5 and the signal-to-noise ratio (SNR) of the received signal is -16dB, it can maintain the probability of false alarm ( ) of 10% while still ensuring the probability of detection ( ) of over 90%.

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Intelligent spectrum sensing algorithm for cognitive internet of vehicles based on KPCA and improved CNN

Cited by 3 publications

References 32 publications

A Review of Research on Spectrum Sensing Based on Deep Learning

A Review of Research on Spectrum Sensing Based on Deep Learning

Fault detection and separation of hybrid electric vehicles based on kernel orthogonal subspace analysis

Cross-attention mechanism-based spectrum sensing in generalized Gaussian noise

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