Improved wavelet transform based edge detection for wide band spectrum sensing in Cognitive Radio

Kumar, Abhishek; Saha, Seemanti; Bhattacharya, Rajarshi

doi:10.1109/usnc-ursi.2016.7588492

Cited by 15 publications

(9 citation statements)

References 5 publications

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“…Of wideband spectrum-sensing’s two techniques, Nyquist-based uses analog-to-digital converters to sample the wideband signals at the Nyquist rate, which can result in a high sampling rate and power consumption. Techniques under this type include wavelet detection [71,72,73,74,75,76], multi-band joint detection [77,78], and filter bank based sensing [79,80,81,82]. Compressive sensing techniques sample signals below the Nyquist rate to reduce the high sampling rate [83,84,85,86,87,88,89].…”

Section: Classificationmentioning

confidence: 99%

“…As shown in Figure 6, the wideband signal for this approach is decomposed into elementary building blocks of sub-bands, characterized by local irregularities in the frequency domain. The wavelet transform is then applied to detect the local spectral irregular structure, which carries important information about the frequency locations and power spectral densities of the sub-bands [71,72,73,74,75,76].…”

Section: Wideband Spectrum Sensingmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

Arjoune

Kaabouch

2019

Sensors

328

196

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Cognitive radio technology has the potential to address the shortage of available radio spectrum by enabling dynamic spectrum access. Since its introduction, researchers have been working on enabling this innovative technology in managing the radio spectrum. As a result, this research field has been progressing at a rapid pace and significant advances have been made. To help researchers stay abreast of these advances, surveys and tutorial papers are strongly needed. Therefore, in this paper, we aimed to provide an in-depth survey on the most recent advances in spectrum sensing, covering its development from its inception to its current state and beyond. In addition, we highlight the efficiency and limitations of both narrowband and wideband spectrum sensing techniques as well as the challenges involved in their implementation. TV white spaces are also discussed in this paper as the first real application of cognitive radio. Last but by no means least, we discuss future research directions. This survey paper was designed in a way to help new researchers in the field to become familiar with the concepts of spectrum sensing, compressive sensing, and machine learning, all of which are the enabling technologies of the future networks, yet to help researchers further improve the efficiently of spectrum sensing.

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Section: Classificationmentioning

confidence: 99%

Section: Wideband Spectrum Sensingmentioning

confidence: 99%

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

Arjoune

Kaabouch

2019

Sensors

328

196

View full text Add to dashboard Cite

show abstract

“…The main drawback of these methods is that they require prefixed bandwidth locations which may cause detection errors in realistic scenarios when signals exceed frequency bins boundaries. Wavelet methods were adopted to solve this problem by detecting edges in the PSD of wideband channel [26,27]. Unlike the above mentioned methods, subspace based techniques are highly promising for wideband spectrum sensing since they can sense multiple channels in one go [15,28] which makes them the most convenient tools for GSM channels detection.…”

Section: Related Workmentioning

confidence: 99%

GSM‐RF Channel Characterization Using a Wideband Subspace Sensing Mechanism for Cognitive Radio Networks

Barrak

Gonnouni

Serrano

et al. 2018

Wireless Communications and Mobile Computing

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In this paper, we examine a spectrum sharing opportunities over the existing Global System of Mobile Communication (GSM) networks, by identifying the unused channels at a specific time and location. For this purpose, we propose a wideband spectrum sensing mechanism to analyze the status of 51 channels at once, belonging to the 10 MHz bandwidth centered at the frequency 945 MHz, in four different areas. We propose a subspace based spectral estimation mechanism, adapted to deal with real measurements. The process begins with data collection using Secondary User (SU) device enabled with Software Defined Radio (SDR) technology, configured to operate in the GSM band. Obtained samples are used then to feed the sensing mechanism. Spectral analysis is delivered to estimate power density peaks and corresponding frequencies. Decision making phase brings together power thresholding technique and GSM control channel decoding to identify idle and busy channels. Experiments are evaluated using detection and false alarm probabilities emulated via Receiver Operating Characteristic (ROC) curves. Obtained performances show better detection accuracy and robustness against variant noise/fading effects, when using our mechanism compared to Energy Detection (ED) based ones as Welch method, and Beamforming based ones as Minimum Variance Distortionless Response (MVDR) method. Occupancy results exhibit considerable potential of secondary use in GSM based primary network.

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“…Narrowband spectrum sensing is used to verify the presence of primary users and is further categorized into various techniques such as energy detection [102]- [105], matched filter detection [106]- [108], feature detection [109]- [112], and eigenvalue based detection [113], [114]. Similarly, wideband spectrum sensing is used to detect the available spectrum holes which is classified based on Nyquist and sub-Nyquist sampling [115], multiband sensing [116]- [119], wavelet-based sensing [120]- [122], filter-bank based sensing [123]- [127], compressed sensing [128]- [135], and multi-coset sensing [136]- [139].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Energy and Spectrum Harvesting Technologies and Protocols for Next Generation Wireless Networks

et al. 2021

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Improved wavelet transform based edge detection for wide band spectrum sensing in Cognitive Radio

Cited by 15 publications

References 5 publications

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

GSM‐RF Channel Characterization Using a Wideband Subspace Sensing Mechanism for Cognitive Radio Networks

A Survey of Energy and Spectrum Harvesting Technologies and Protocols for Next Generation Wireless Networks

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