Wavelet transform for spectrum sensing in Cognitive Radio networks

Zhao, Yu; Wu, Yuanyuan; Wang, Jian; Zhong, Xuexia; Mei, Lin

doi:10.1109/icalip.2014.7009857

Cited by 18 publications

(8 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…Other authors proposed to use compressive sensing without recovery. For instance, the authors of [71,72,73] stated that the process of recovery introduces greater complexity to the system, and suggest not recovering the wideband signal as the objective of spectrum sensing is to determine the locations of the active frequency channels. The author of [71] proposed an algorithm that enables the detection of the primary user signal directly from the compressed measurements without using the recovery process because the measurement matrix was designed to preserve the energy of the PU signal in only a few samples.…”

Section: Wideband Spectrum Sensingmentioning

confidence: 99%

See 2 more Smart Citations

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

Arjoune

Kaabouch

2019

Sensors

345

196

View full text Add to dashboard Cite

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.

show abstract

Section: Classificationmentioning

confidence: 99%

Section: Wideband Spectrum Sensingmentioning

confidence: 99%

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

345

196

View full text Add to dashboard Cite

show abstract

“…Wavelet transformation algorithm is to be applied in determining whether the original received wave from antenna holds signals or not ie H1 or H0. For this we use an algorithm called WATRAB [16]. Here, we use a mixer and a combination of low pass filters and daubechies db4 wavelet transform is then applied.…”

Section: Adaptive Sensing Using Wavelet Detection Instead Of Eigen Vamentioning

confidence: 99%

Adaptive Spectrum Sensing in Cognitive Radio Networks

Gafoor¹,

Kuriakose²,

M³

et al. 2018

IJCA

View full text Add to dashboard Cite

The available radio spectrum is not used efficiently, therefore a new technology called Cognitive Radio (CR) is used to increase the spectrum utilization. The objective of CR is to use the available spectrum efficiently without any interference to the Primary Users (PUs). Spectrum sensing plays an essential part in cognitive radio networks inorder to obtain spectrum awareness. Energy detection, matched filter detection, cyclostationary detection etc are the most commonly used techniques for spectrum sensing.This paper proposes an Adaptive spectrum sensing technique in which a particular sensing method from matched filter detection, Energy detection or Wavelet based detection is chosen according to the information available and SNR of the received signal. This paper also investigates the performance of both Eigen value and Wavelet based sensing in low SNR regions.

show abstract

“…For instance, the authors of [ 8 , 9 , 10 , 11 ] performed spectrum measurements to find the spectrum usage pattern for broad frequency bands. To overcome the limitations of the models proposed in [ 8 , 9 , 10 , 11 ], some recent innovative techniques have been proposed such as wavelet-based detection [ 12 , 13 , 14 ], multi-band joint detection [ 15 ], and filter-band-based sensing [ 16 , 17 , 18 ]. The wavelet-based detection approach is an edge-based detection that characterizes the edges of the occupied channels.…”

Section: Introductionmentioning

confidence: 99%

Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach

Arjoune

Kaabouch

2018

Sensors

View full text Add to dashboard Cite

Sensing the wideband spectrum is an important process for next-generation wireless communication systems. Spectrum sensing primarily aims at detecting unused spectrum holes over wide frequency bands so that secondary users can use them to meet their requirements in terms of quality-of-service. However, this sensing process requires a great deal of time, which is not acceptable for timely communications. In addition, the sensing measurements are often affected by uncertainty. In this paper, we propose an approach based on Bayesian compressive sensing to speed up the process of sensing and to handle uncertainty. This approach takes only a few measurements using a Toeplitz matrix, recovers the wideband signal from a few measurements using Bayesian compressive sensing via fast Laplace prior, and detects either the presence or absence of the primary user using an autocorrelation-based detection method. The proposed approach was implemented using GNU Radio software and Universal Software Radio Peripheral units and was tested on real-world signals. The results show that the proposed approach speeds up the sensing process by minimizing the number of samples while achieving the same performance as Nyquist-based sensing techniques regarding both the probabilities of detection and false alarm.

show abstract

Wavelet transform for spectrum sensing in Cognitive Radio networks

Cited by 18 publications

References 7 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

Adaptive Spectrum Sensing in Cognitive Radio Networks

Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach

Contact Info

Product

Resources

About