A Novel Low-Complexity Cyclostationary Feature Detection Using Sub-Nyquist Samples for Wideband Spectrum Sensing

Mathew, Sneha Gem; Samuel, Chris Prema

doi:10.1007/s00034-021-01771-0

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…In Ref. [33], scholars extracted the cyclostationary feature from broadband signals from sub-Nyquist samples in order to identify modulation schemes with low computational complexity, and the proposed sub-Nyquist sampling structure of the wideband modulated converter (MWC) was used to perform spectrum detection with good results.…”

Section: Cyclostationary Feature Detectionmentioning

confidence: 99%

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: Cyclostationary Feature Detectionmentioning

confidence: 99%

A Review of Research on Spectrum Sensing Based on Deep Learning

Zhang,

Luo

2023

Electronics

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Robust Parameter Measurement of PSK Signals Based on FRI Sampling and Structured Sparsity

Wei,

Fu,

Jiang

et al. 2023

IEEE Trans. Instrum. Meas.

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Improving and analysing the spectral access performance of QAM-64 optical NOMA using a hybrid ED-CSD algorithm

Kumar,

Gaur,

Chakravarti

2023

Journal of Optical Communications

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Cognitive radio (Cr) is one of among the most promising developments, which combines optical non-orthogonal multiple access (O-NOMA) methods to increase the fifth generation (5G) spectrum’s usage efficiency. In this work, we provide a hybrid approach based on cyclic stationary feature detection (CFD) and energy detection (ED). The probability of false alarm (PFA), on which it is based, is used to choose which spectrum sensing (SS) approach to use. When PFA is less than 0.5, the ED is chosen; otherwise, the MF is. The suggested detection method in O-NOMA is investigated using Matlab-2016 simulation with reference to several metrics, such as bit error rate (BER), signal-to-noise ratio (SNR), probability of detection (PD), and PFA.

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A Novel Low-Complexity Cyclostationary Feature Detection Using Sub-Nyquist Samples for Wideband Spectrum Sensing

Cited by 3 publications

References 20 publications

A Review of Research on Spectrum Sensing Based on Deep Learning

A Review of Research on Spectrum Sensing Based on Deep Learning

Robust Parameter Measurement of PSK Signals Based on FRI Sampling and Structured Sparsity

Improving and analysing the spectral access performance of QAM-64 optical NOMA using a hybrid ED-CSD algorithm

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