An energy detection based signal detection method with multiple antennas in cognitive radio

Luo, Jun; Zhang, Guoping; Xu, Hongbo; Zhou, Yu Rong; Yang, Weidong

doi:10.1109/iaeac.2015.7428544

Cited by 3 publications

(6 citation statements)

References 11 publications

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“…Next, in the ED [3], the MME [13], the EME [6], the CAV [7] and the SVM [23] schemes, the spectrum sensing schemes are compared in term of P e under different SNRs with different N . For the SVM and the proposed scheme, the number of testing sample are all selected as 1500.…”

Section: B Performance Comparisons Of the Spectrum Sensing Schemesmentioning

confidence: 99%

“…Fig. 11 exhibits the ROC situations between the energy detection [3] and the machine learning schemes, including k-means [17], the KNN [19] and the SVM schemes. At each SNR, the relationship of P d ∼ P f is obtained and shown in Fig.…”

Section: B Performance Comparisons Of the Spectrum Sensing Schemesmentioning

confidence: 99%

“…13. The ED [3], MME [13], the EME [6], the CAV [7], and the SVM [23] schemes are compared with our proposed one in term of normalized sensing time in Fig. 13.…”

Section: B Performance Comparisons Of the Spectrum Sensing Schemesmentioning

confidence: 99%

“…The ED method without prior information of a primary user (PU) usually had low computational complexity. However, it was sensitive to the uncertainty of noise range and it could not distinguish a PU signal from noises at low signal-to-noise ratios (SNRs) [3]. Despite of the short detection duration and satisfactory precision in matched filter detection, it required prior information of PUs in advanced and it had high computational complexity [4].…”

Section: Introductionmentioning

confidence: 99%

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Improved Blind Spectrum Sensing by Covariance Matrix Cholesky Decomposition and RBF-SVM Decision Classification at Low SNRs

et al. 2019

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An improved blind spectrum sensing scheme is established by the covariance matrix Cholesky decomposition and radial basis function (RBF)-support vector machine (SVM) decision classification at low signal-to-noise ratios (SNRs). Under strong background noises, the proposed scheme improves the recognition rate of primary users (PUs) than that of the current blind spectrum sensing. First, the ratio of the maximum-to-minimum eigenvalue of a covariance matrix obtained by the Cholesky decomposition is used to construct the statistics. Second, the statistics are labeled with ''+1'' or ''−1,'' namely, the energy characteristics of the training samples are extracted and marked with ''+1'' for PUs and ''−1'' for noises. Finally, an RBF-SVM classification model, with an intelligent RBF as the SVM kernel function, is obtained by training the above-mentioned statistics and the labels. Thus, the received signals are classified as PUs or not be trained in the SVM model. The threshold possesses self-learning ability, and it distinguishes PU signals from noises effectively. The classification among PU signals and noises is implemented by the optimal SVM decision boundary, derived from maximizing the margin of the decision boundary of trained samples for efficient detection. In addition, the complexity of the statistic construction is lower than that of the conventional maximum minimum eigenvalue (MME). The simulation results show that the RBF in our scheme has 77.5% accuracy at −10 dB, and it outperforms linear kernel function significantly by about 27.5% in accuracy at −10 dB. In addition, the average error probability of the proposed scheme is reduced by about 26% when compared with those of original SVM schemes at −20 dB. The proposed scheme also outperforms the current MME detection in detection probability over 10% at −20 dB. Therefore, the proposed blind spectrum sensing scheme can be efficiently used to detect the PUs by the covariance matrix Cholesky decomposition and the RBF-SVM decision classification in the fifth-generation (5G) communications, especially at low SNRs.

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Section: B Performance Comparisons Of the Spectrum Sensing Schemesmentioning

confidence: 99%

Section: B Performance Comparisons Of the Spectrum Sensing Schemesmentioning

confidence: 99%

“…13. The ED [3], MME [13], the EME [6], the CAV [7], and the SVM [23] schemes are compared with our proposed one in term of normalized sensing time in Fig. 13.…”

Section: B Performance Comparisons Of the Spectrum Sensing Schemesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Blind Spectrum Sensing by Covariance Matrix Cholesky Decomposition and RBF-SVM Decision Classification at Low SNRs

et al. 2019

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“…Signal detection [1][2][3][4][5] technology is designed to judge the presence or absence of signals from the received data, and further realize the time-frequency positioning of signals. It is the basis for the next step of signal modulation pattern recognition 6 , demodulation decoding, individual recognition 7 , protocol analysis, and intelligence generation, and is the first door to communication detection and countermeasure system.…”

Section: Introductionmentioning

confidence: 99%

A simple baseline for signal detection and extraction

Su,

Gu,

Zhang

et al. 2023

Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023)

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In the context of modern information warfare, wireless communication technology has been widely used. The transmission mode of wireless communication is to use electromagnetic waves in the air to carry out information exchange. Compared with the traditional way of transmitting information through cables, wireless communication has better adaptability, expansibility, and easy maintenance. With the rapid development of wireless communication technology, the number of various electromagnetic equipment is increasing, and the communication signal system and modulation modes are becoming more and more complex and diversified. The overlapping of various electromagnetic signals makes the electromagnetic environment increasingly complicated. It has become a key problem that needs to be solved in the field of communication countermeasures to quickly discover signals from the broadband spectrum and locate important enemy signals through individual recognition, protocol analysis, and other technologies. In this paper, we propose a simple baseline approach for signal detection and extraction. First, we store the received IQ signal in a buffer pool. In order to realize real-time processing for broadband signals, we first perform blind estimation of signal to noise ratio (SNR), and filter out the signals based on the estimated SNR, and then only process the signals whose SNR is greater than a certain threshold. Then we detect the filtered signal by fast Fourier transform and threshold comparison from the frequency domain, and locate the time domain and frequency domain position of the signal. Our experiments demonstrate that our proposed approach can effectively detect the signals in real-time. When the SNR is greater than 3db, the signal detection error is not more than 0.01.

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