Design of Cosine Modulated Filter Banks exploiting spline function for spectrum sensing in Cognitive Radio applications

Sharma, Komal; Sharma, Anu

doi:10.1109/icpeices.2016.7853205

Cited by 9 publications

(6 citation statements)

References 8 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%

“…Wideband spectrum sensing usually involves estimation of power spectral density (PSD). One way to estimate this function is to use the filter bank approach [79,80,81,82] which can enable an efficient implementation of band-pass filters by using a poly-phase decomposition of the prototype filter [79]. The concept of filter bank analysis, as shown in Figure 8, consists of an array of band-pass filters.…”

Section: Wideband Spectrum Sensingmentioning

confidence: 99%

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

Arjoune

Kaabouch

2019

Sensors

343

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

343

196

View full text Add to dashboard Cite

show abstract

“…Power spectral density (PSD) is usually estimated with wideband SS. Using a poly-phase decomposition of the prototype filter [23] can be an effective means of estimating this function using the filter bank approach [23][24][25][26]. An array of band-pass filters forms the basis of filter bank analysis, as shown in figure 12.…”

Section: Filter Bank-based Sensingmentioning

confidence: 99%

Investigation on Cognitive Radio Networks: Introduction, Spectrum Sensing, Ieee Standards, Challenges, Applications

Kumar Vaigandla,

Mounika,

Urooj

et al. 2022

iJEAST

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During the last decade, Cognitive Radio (CR) has become a popular research topic. The availability of radio spectrum is in shortage, and CR technology can solve the problem by enabling dynamic spectrum access. This innovative technology has been used to manage the radio spectrum since it was introduced. These developments have led to rapid advances in this research field. A review of recent advances in Spectrum Sensing (SS) is presented in this paper, from its origins to its present state. A CR network has been found to be a highly effective and intelligent technology. Frequency spectrum is a bounded natural resource and an essential component of wireless communication networks. We explored CR in this paper and its various phases. Different definitions of CR are then presented from various institutions. Many different types of communications systems use CR techniques. It is anticipated that they will improve commercial and military data services, as well as increase the use of underutilized radio frequencies. In this paper, CR standards have been discussed, as well as its applications in various areas.

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“…Among these techniques, sensing techniques based on Nyquist and sub-Nyquist have shown promise, as some authors point out in [18]. In Nyquist wideband sensing techniques, the received signal at some point is sampled by a traditional analog-to-digital converter with a Nyquist sampling rate ( f s > 2 f m ) [19]; as an advantage, these techniques present a simple structure, and as disadvantages, high sampling rate and energy cost [20]. The sub-Nyquist-based techniques overcome the disadvantages presented in Nyquist-based SS by reducing the sampling rate and then detecting spectral holes with the partial data that remained [21]; as an advantage, these techniques present a low sampling rate, and as disadvantage, they are sensitive to design imperfections [22].…”

Section: Introductionmentioning

confidence: 99%

Deep Cooperative Spectrum Sensing Based on Residual Neural Network Using Feature Extraction and Random Forest Classifier

Valadao

Amoedo

Costa

et al. 2021

Sensors

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Some bands in the frequency spectrum have become overloaded and others underutilized due to the considerable increase in demand and user allocation policy. Cognitive radio applies detection techniques to dynamically allocate unlicensed users. Cooperative spectrum sensing is currently showing promising results. Therefore, in this work, we propose a cooperative spectrum detection system based on a residual neural network architecture combined with feature extractor and random forest classifier. The objective of this paper is to propose a cooperative spectrum sensing approach that can achieve high accuracy in higher levels of noise power density with less unlicensed users cooperating in the system. Therefore, we propose to extract features of the sensing information of each unlicensed user, then we use a random forest to classify if there is a presence of a licensed user in each band analyzed by the unlicensed user. Then, information from several unlicensed users are shared to a fusion center, where the decision about the presence or absence of a licensed user is accomplished by a model trained by a residual neural network. In our work, we achieved a high level of accuracy even when the noise power density is high, which means that our proposed approach is able to recognize the presence of a licensed user in 98% of the cases when the evaluated channel suffers a high level of noise power density (−134 dBm/Hz). This result was achieved with the cooperation of 10 unlicensed users.

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Design of Cosine Modulated Filter Banks exploiting spline function for spectrum sensing in Cognitive Radio applications

Cited by 9 publications

References 8 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

Investigation on Cognitive Radio Networks: Introduction, Spectrum Sensing, Ieee Standards, Challenges, Applications

Deep Cooperative Spectrum Sensing Based on Residual Neural Network Using Feature Extraction and Random Forest Classifier

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