Wideband Temporal Spectrum Sensing Using Cepstral Features

Cheng, Hanke; Mark, Brian L.; Ephraim, Yariv

doi:10.1109/wowmom.2019.8793002

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…For instance, the authors in [29] have employed CA techniques in waveform classification and for detecting OFDM signals and also for estimating their parameters. Moreover, the authors of [30] have introduced a WideBand Temporal Sensing (WBTS) approach based on a cepstral envelope detector. Precisely, the involvement of the cepstrum-based spectrum envelope detector is to adapt to dynamic changes that may occur in the configuration of a PU channel.…”

Section: B Application Of Cepstral Analysis In Cognitive Radiomentioning

confidence: 99%

“…The rationale of this approach is to use a cepstral feature vector to detect the changes in the spectrum envelope of a PU signal within a given frequency band. Based on the recursive temporal spectrum sensing algorithm proposed in [31], the authors in [30] have proposed the use of cepstral analysis to monitor the change of the PU's configuration instead of the conventional ED front end.…”

Section: B Application Of Cepstral Analysis In Cognitive Radiomentioning

confidence: 99%

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A Wideband Spectrum Sensing Approach for Cognitive Radios Based on Cepstral Analysis

Moawad

Yao²,

Mansour

et al. 2020

IEEE Open J. Commun. Soc.

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Multiband spectrum access plays an essential role in cognitive radio systems so as to increase the network's throughput through wideband spectrum sensing. It includes identifying the number of subbands comprising a wide spectrum by edge detection, and also examining their occupancy through primary user detection techniques. Despite the offered accuracy of the wavelet-based approaches, their complexity becomes a drawback. Remarkably, the features revealing property of cepstral analysis and its implementation simplicity make it a suitable candidate for signal detection. Motivated by these reasons, this paper presents a wideband spectrum sensing approach based on cepstral analysis. First, we propose the differential log spectral density algorithm for the edge detection phase in order to detect the spectral boundaries within the wideband of interest. Also, we present a mathematical framework of the proposed algorithm and an expression for the detection threshold of the proposed detector is derived. The simulation results have showed a superior performance of the edge detection algorithm to different wavelet-based techniques at low-to-medium noise power. Used in conjunction with denoising, the proposed edge detector shows good detection results at low signal-to-noise ratio. For the primary user detection phase, we introduce the improved passband autocepstrum detector to tackle the misdetection problem of noise-like signals and it outperforms different state-of-the-art techniques. Finally, the uncertainty problem of the subbands center frequencies is addressed and the baseband autocepstrum detector is introduced as a potential solution to improve signal detection in frequency selective fading.

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Section: B Application Of Cepstral Analysis In Cognitive Radiomentioning

confidence: 99%

Section: B Application Of Cepstral Analysis In Cognitive Radiomentioning

confidence: 99%

A Wideband Spectrum Sensing Approach for Cognitive Radios Based on Cepstral Analysis

Moawad

Yao²,

Mansour

et al. 2020

IEEE Open J. Commun. Soc.

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“…Cepstral features calculated from vibration data of onvehicle motion sensors are used to detect some events in [6]. In the wireless communication fields, cepstral features are used to analyze the channel utilization state of primary users in [7] and identify modulation technique [8] but there are few examples using cepstral features for detection of electromagnetic noise. Focusing on features for detection of noise and signal, if data is investigated previously and information about characteristics of frequency is not needed, only using average and deviation values of power spectrum will work [9].…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic wave pattern detection using cepstral features in the manufacturing field

et al. 2020

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In manufacturing fields such as factories, multiple wireless communication systems often operate in the same area simultaneously. Moreover, it is known that industrial equipment emits electromagnetic noise over channels for wireless communications [1]. In order to ensure reliable communications under such an environment, monitoring radio wave environments specific to each manufacturing field and finding channels and timing which enable stable communications are required. The authors have studied technologies to efficiently analyze a large amount of monitoring data including signals which show unknown spectrum such as wide band electromagnetic noise [2,3]. This paper proposes performing machine learning using cepstrum vectors as features to grasp types of noise and signals from data measured under environments in which electromagnetic noise and communication signals coexist. By using this features, the authors demonstrate that reduction of computational loads and improvement of detection accuracy can be expected.

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