Spectrum sensing for cognitive radio network with multiple receive antennas under impulsive noise environments

Lee, Seungwon; Park, So Ryoung; Kim, Yun Hee; Song, Iickho

doi:10.23919/jcn.2021.000016

Cited by 14 publications

(6 citation statements)

References 27 publications

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“…A distribuição SαS é amplamente utilizada na modelagem do ruído impulsivo nos artigos mais recentes que analisam o desempenho do sensoriamento espectral em redes de rádios cognitivos. Citam-se [9]- [14] como exemplos.…”

Section: B Trabalhos Relacionados E Estrutura Do Artigounclassified

Análise das Distribuições SαS e αSG na Modelagem do Ruído de Camarão-de-Estalo

Guedes¹,

Guimarães²

2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-Sistemas de comunicação subaquáticos por ondas acústicas estão sujeitos à degradação de desempenho causada pelo ruído impulsivo gerado pelo crustáceo camarão-de-estalo. Em análises matemáticas ou simulações, esse ruído é comumente modelado por meio de amostras temporalmente descorrelacionadas seguindo a distribuição alfa-estável simétrica (symmetric alpha-stable, SαS). Neste artigo analisa-se a adequação dessa distribuição em contraste com a distribuição alfa-sub-Gaussiana (alpha-sub-Gaussian, αSG) com correlação temporal não nula, no contexto do sensoriamento espectral subaquático.Palavras-Chave-Camarão-de-estalo, comunicação subaquática, ruído impulsivo, sensoriamento espectral.

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Section: B Trabalhos Relacionados E Estrutura Do Artigounclassified

Análise das Distribuições SαS e αSG na Modelagem do Ruído de Camarão-de-Estalo

Guedes¹,

Guimarães²

2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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“…It has been found that the SαS distribution is widely used in impulsive noise modeling in the most recent articles that analyze the performance of spectrum sensing in cognitive radio networks. References [20]- [25] are cited as examples. In [17], the adequacy of the SαS distribution is analyzed in contrast to the αSG distribution with non-zero temporal correlation, in the context of underwater spectrum sensing subjected to snapping shrimp noise.…”

Section: A Related Researchmentioning

confidence: 99%

Direct-Conversion Spectrum Sensor Impaired by Symmetric α-Stable and α-Sub-Gaussian Noises

Guedes

Guimarães²

2023

JCIS

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Spectrum sensing in underwater cognitive acoustic networks (UCANs) can be impaired by impulsive noise generated by snapping shrimps. In mathematical analysis or simulations, the amplitude variations of this noise are commonly modeled by the symmetric alpha-stable (SS) distribution. As an alternative, the alpha-sub-Gaussian (SG) distribution can model both temporal correlation and amplitude variations. This article assesses the performance of underwater spectrum sensing with a direct-conversion receiver (DCR) under impulsive noise modeled by the SS and SG distributions. Several recent test statistics are compared, demonstrating that they have different degrees of robustness against impulsive noise and that the DCR is significantly less sensitive to this noise, compared to the conventional receiver model that does not take into account the influences of hardware characteristics into the performance of spectrum sensing.

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“…Various methods have been proposed by the researchers. Energy detection, cyclostationary feature detection, and matched filter [22] are the most commonly used methods of spectrum sensing. The probability of detection (P d ) and probability of false alarm (P f ) are among the important parameters used in spectrum sensing.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

Raza,

Ali,

Ehsan

et al. 2023

Sensors

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The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients’ health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient’s health data by exploiting the primary user’s (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection (Pd) and probability of false alarm (Pf). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE).

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Spectrum sensing for cognitive radio network with multiple receive antennas under impulsive noise environments

Abstract: Article that has been accepted for inclusion in a future issue of a journal. Content is final as presented, with the exception of pagination.

Cited by 14 publications

References 27 publications

Análise das Distribuições SαS e αSG na Modelagem do Ruído de Camarão-de-Estalo

Análise das Distribuições SαS e αSG na Modelagem do Ruído de Camarão-de-Estalo

Direct-Conversion Spectrum Sensor Impaired by Symmetric α-Stable and α-Sub-Gaussian Noises

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

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