Wavelet Based Spectrum Sensing Techniques for Cognitive Radio - A Survey

Divakaran, Karthik; P, Naveen Manikandan; R, Shri Hari R

doi:10.5121/ijcsit.2011.3209

Cited by 12 publications

(3 citation statements)

References 16 publications

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“…Now the research into spectrum sensing technology can be divided into non-cooperative spectrum sensing and cooperative spectrum sensing. The non-cooperative spectrum sensing technology is the main spectrum sensing technology in CWSN which can be summarized as follows [11,12]: energy detectors [13,14,15,16], feature detection [17,18,19,20], and matched filter detection [21,22,23]. The approaches are summarized in Table 1, which shows the algorithm, its limitation and the relevant recent references [24].…”

Section: Related Workmentioning

confidence: 99%

Optimized Non-Cooperative Spectrum Sensing Algorithm in Cognitive Wireless Sensor Networks

Chen

Yin

et al. 2019

Sensors

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The cognitive wireless sensor network (CWSN) is an important development direction of wireless sensor networks (WSNs), and spectrum sensing technology is an essential prerequisite for CWSN to achieve spectrum sharing. However, the existing non-cooperative narrowband spectrum sensing technology has difficulty meeting the application requirements of CWSN at present. In this paper, we present a non-cooperative spectrum sensing algorithm for CWSN, which combines the multi-resolution technique, phase space reconstruction method, and singular spectrum entropy method to sense the spectrum of narrowband wireless signals. Simulation results validate that this algorithm can greatly improve the detection probability at a low signal-to-noise ratio (SNR) (from −19dB to −12dB), and the detector can quickly achieve the best detection performance as the SNR increases. This algorithm could promote the development of CWSN and the application of WSNs.

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Section: Related Workmentioning

confidence: 99%

Optimized Non-Cooperative Spectrum Sensing Algorithm in Cognitive Wireless Sensor Networks

Chen

Yin

et al. 2019

Sensors

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“…For better results and more accurate estimates of the channel edges, the wavelet transform of the signal at multiple scales (namely, dyadic scales) is considered. There are mainly two methods to combine dyadic scales, which both involve upsampling the signals at higher scales to match the length of the wavelet transform obtained at the first level: the multiscale wavelet sum and the multiscale wavelet product . Each of these two methods of combination has its advantages and disadvantages.…”

Section: Wavelet‐based Basis Matricesmentioning

confidence: 99%

A stationary wavelet transform approach to compressed spectrum sensing in cognitive radio

El‐Khamy

Abd‐el‐Malek

Kamel

2016

Int J Communication

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SUMMARYIn this paper, we introduce the implementation of the stationary wavelet transform in compressive sensing, particularly in spectrum sensing and edge detection in cognitive radio. We also review the different forms of the compressive sensing basis matrix, providing a comparative study of their performance, with emphasis on the matrices implementing the discrete wavelet transform, the stationary wavelet transform, and their multiscale counterparts. The results presented in this paper show that, using the stationary wavelet transform, we can reach the performance level and error rate obtained by the discrete wavelet transform using only half the samples required by the latter to attain the same performance.

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“…6.5.1 Wavelet Based Detection [48][49] By precisely locating the spectrum boundaries one can effectively prevent the problem of interference between adjacent bands and increase the data rates in the channel. Of various attempts that have been made to accurately detect the spectral boundaries, Wavelet Edge detection is a method which is widely utilized; however the efficiency of this system is highly dependent upon the type of wavelet used.…”

Section: Other Sensing Techniquesmentioning

confidence: 99%

Assorted Facets of Physical Layer in Cognitive Radio: A Review

Verma¹,

Choudhary²,

Yarde³

2012

IJCA

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The intensification in the number of mobile subscribers over the preceding years leads to a situation of the Voice-oriented, data-oriented wireless telephony. It is now point in time to explore new demands and to find the new ways to broaden the mobile concepts. The Cognitive Radio (CR) is one of the essential steps that initiate the solution towards these problems. Then also there are lot of challenges associated to Cognitive Radio in the area of operational field and implementation in Real world. In this paper we tried to give the ideas about the voyage of Cognitive Radio from evolution to challenges experienced with respect to Physical Layer.

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Wavelet Based Spectrum Sensing Techniques for Cognitive Radio - A Survey

Abstract: ABSTRACT

Cited by 12 publications

References 16 publications

Optimized Non-Cooperative Spectrum Sensing Algorithm in Cognitive Wireless Sensor Networks

Optimized Non-Cooperative Spectrum Sensing Algorithm in Cognitive Wireless Sensor Networks

A stationary wavelet transform approach to compressed spectrum sensing in cognitive radio

Assorted Facets of Physical Layer in Cognitive Radio: A Review

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