A novel method of blind signal detection using the distribution of the bin values of the power spectrum density and the moving average

Nikonowicz, Jakub; Jessa, M.

doi:10.1016/j.dsp.2017.04.002

Cited by 8 publications

(8 citation statements)

References 24 publications

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“…Among them, energy detection stands out due to its effectiveness in wideband sensing when the information of primary user signals is not provided. Towards this end, different variants of energy-based method are proposed for blind spectrum sensing [34]- [36], and applied in different communication systems including OFDM [37] and massive MIMO [38]. In this work, we propose a new energy-based multi-signal blind sensing algorithm for the task of morse detection, which can be easily integrated into the deep learning module.…”

Section: Blind Spectrum Sensingmentioning

confidence: 99%

DeepMorse: A Deep Convolutional Learning Method for Blind Morse Signal Detection in Wideband Wireless Spectrum

et al. 2019

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With the recent advances in wireless technologies, high frequency radio has become the primary medium for long-distance communication. Among various types of modulation in signal transmission, morse code stands out due to its simplicity and efficiency in information transmission while costing small bandwidth. In practice, however, it is extremely laborious to locate morse signals in wideband communication. It's a needle in a haystack, if morse code is sent in random carrier waves at random period. To avoid this, automatic morse signal detection has become a challenging task in wireless morse communications, and new solutions could be derived from the latest machine learning techniques. In this paper, we propose a deep learning framework, namely DeepMorse, to blindly detect morse signals in wideband spectrum data. In particular, we first develop a multi-signal sensing module to retrieve signal candidates from wideband spectrum without prior knowledge. Then, we construct a CNN-based module to extract informative features from the located candidates, in order to distinguish the morse signal from other types of modulation. To evaluate the proposed DeepMorse model, we set up a testbed utilizing commercialized long-distance wireless communication devices. The experimental results demonstrate that DeepMorse is able to effectively detect morse signals and outperform the state-of-the-art methods on four real-world datasets. INDEX TERMS Deep learning, blind signal detection, morse code, wideband wireless spectrum.

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Section: Blind Spectrum Sensingmentioning

confidence: 99%

DeepMorse: A Deep Convolutional Learning Method for Blind Morse Signal Detection in Wideband Wireless Spectrum

et al. 2019

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“…Zhang et al [ 10 , 14 ] presented a spectrum sensing scheme based on the entropy of the spectrum magnitude, which has been used in many practical applications. For example, Jakub et al [ 15 ] used this scheme to assist in blind signal detection, Zhao [ 16 ] improved the two-stage entropy detection method based on this scheme, and Waleed et al [ 17 ] applied this scheme to maritime radar network. So [ 18 ] used the conditional entropy of the spectrum magnitude to detect unauthorized user signals in cognitive radio networks.…”

Section: Introductionmentioning

confidence: 99%

A Novel Blind Signal Detector Based on the Entropy of the Power Spectrum Subband Energy Ratio

Han

Wang

2021

Entropy

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In this paper, we present a novel blind signal detector based on the entropy of the power spectrum subband energy ratio (PSER), the detection performance of which is significantly better than that of the classical energy detector. This detector is a full power spectrum detection method, and does not require the noise variance or prior information about the signal to be detected. According to the analysis of the statistical characteristics of the power spectrum subband energy ratio, this paper proposes concepts such as interval probability, interval entropy, sample entropy, joint interval entropy, PSER entropy, and sample entropy variance. Based on the multinomial distribution, in this paper the formulas for calculating the PSER entropy and the variance of sample entropy in the case of pure noise are derived. Based on the mixture multinomial distribution, the formulas for calculating the PSER entropy and the variance of sample entropy in the case of the signals mixed with noise are also derived. Under the constant false alarm strategy, the detector based on the entropy of the power spectrum subband energy ratio is derived. The experimental results for the primary signal detection are consistent with the theoretical calculation results, which proves that the detection method is correct.

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“…In such a case, the use of GoF and in particular Gaussianity and normality testing methods needs to be reconsidered. To meet new challenges, the authors introduced the foundations of a novel, energy-based Gaussian fitting test [20]. The described solution is based on a weakly dependent, empirical energy distribution of the received signal, which allows simple implementation and potentially high efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we examine and compare the effectiveness of discontinuous signal detection using known statistical fitting methods in general and the method developed in [20] in particular. Method described in [21], which uses a fundamentally different basis of detection, is a benchmark adopted in the conducted research.…”

Section: Introductionmentioning

confidence: 99%

Gaussianity Testing as an Effective Technique for Detecting Discontinuous Transmission in 5G Networks

Nikonowicz

Jessa

2021

IEEE Access

Self Cite

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The paper investigates statistical distribution testing-based detection methods in an intermittent signal detection scenario. The relevance of the research is driven by 5G networks based on packet transmission, incorporating the concept of cognitive radio and adapting spectrum detection methods from Long Term Evolution (LTE) Licensed-Assisted Access (LAA). The conducted study refers to the recently proposed methods based on testing goodness-of-fit (GoF) of statistical distributions, which are compared with a conventional energy detector. The authors examine the applicability of well-known GoF methods in intermittent transmission, as they require reconsideration in 5G communication systems, and investigate the behavior of the innovative energy-based GoF. The experiments are carried out for different transmitter activity factors, i.e., channel occupancy and signal-to-noise ratio (SNR), demonstrating the superiority of the GoF-based methods in general and particularly the invented GoF test over other energy-based detectors for discontinuous signals detection. INDEX TERMSGaussianity testing, energy detection, discontinuous transmission.

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A novel method of blind signal detection using the distribution of the bin values of the power spectrum density and the moving average

Cited by 8 publications

References 24 publications

DeepMorse: A Deep Convolutional Learning Method for Blind Morse Signal Detection in Wideband Wireless Spectrum

DeepMorse: A Deep Convolutional Learning Method for Blind Morse Signal Detection in Wideband Wireless Spectrum

A Novel Blind Signal Detector Based on the Entropy of the Power Spectrum Subband Energy Ratio

Gaussianity Testing as an Effective Technique for Detecting Discontinuous Transmission in 5G Networks

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