Blind Matching Filtering Algorithm for Spectrum Sensing under Multi-Path Channel Environment

Zhang, Changqing; Li, Jin; Li, Bingbing; Ma, Wenping

doi:10.3390/electronics12112499

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…Therefore, the development of this technology is dependent on the innovation of researchers as well as the development of hardware devices. Based on conventional methods [34][35][36] Matched filter detection Better performance than energy detection in ideal environments.…”

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

confidence: 99%

“…The results show that the proposed hybrid matched filter detection method outperforms the traditional matched filter detection method in detecting the presence of PU. While Changqing Zhang investigated the correlation between neighboring received signals under dispersive channels with the idea that different channels have different impacts on sensing, and proposed a blind matched filter (BMF)-based spectrum-sensing algorithm, in [35], which does not need PU a priori information compared to the traditional method and based on the correlation between signals with different time delays, and proposed an improved BMF (IBMF) algorithm. The results of the study showed better detection performance compared to the traditional matched filter detection method.…”

Section: Matched Filter Detectionmentioning

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%

Section: Matched Filter Detectionmentioning

confidence: 99%

A Review of Research on Spectrum Sensing Based on Deep Learning

Zhang,

Luo

2023

Electronics

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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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