A robust multistage spectrum sensing model for cognitive radio applications

Mourougayane, Kaliappan; Amgothu, Bhadru; Bhagat, Sanket; Subramanian, S.V.

doi:10.1016/j.aeue.2019.152876

Cited by 16 publications

(9 citation statements)

References 16 publications

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“…The authors of the study [19] have proposed the solution as a reputation-based mechanism, wherein the issues of falsification of data based on the weightage of the sensing reports are the structure. Authors of a research study [20] have discussed the conditions of outlier detection schemes [21] is about the conversion of the areas of interest into a grid form for identification and discarding of the outlier conditions. Another contemporary model [22] has discussed the option of abnormality detection as a scope, and in [23], the abnormality detection model has emphasized with a model of path-loss exponent over the signal propagation.…”

Section: Related Workmentioning

confidence: 99%

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Affirmed Crowd Sensor Selection based Cooperative Spectrum Sensing

Rao¹,

Prasad²,

Prasad³

2022

IJRITCC

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The Cooperative Spectrum sensing model is gaining importance among the cognitive radio network sharing groups. While the crowd-sensing model (technically the cooperative spectrum sensing) model has positive developments, one of the critical challenges plaguing the model is the false or manipulated crowd sensor data, which results in implications for the secondary user’s network. Considering the efficacy of the spectrum sensing by crowd-sensing model, it is vital to address the issues of falsifications and manipulations, by focusing on the conditions of more accurate determination models. Concerning this, a method of avoiding falsified crowd sensors from the process of crowd sensors centric cooperative spectrum sensing has portrayed in this article. The proposal is a protocol that selects affirmed crowd sensor under diversified factors of the decision credibility about spectrum availability. An experimental study is a simulation approach that evincing the competency of the proposal compared to the other contemporary models available in recent literature.

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Section: Related Workmentioning

confidence: 99%

“…Some of the solutions proposed in the similar format are [27], [28] wherein the public-key schemes-based models have proposed for defending the conditions of malicious users' networks. However, the compromised feedback updates by secondary users haven't been addressed in these contributions [21]- [26].…”

Section: Related Workmentioning

confidence: 99%

Affirmed Crowd Sensor Selection based Cooperative Spectrum Sensing

Rao¹,

Prasad²,

Prasad³

2022

IJRITCC

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“…A multi-stage robust detector suitable for CR was reported by Kaliappan et al [18]. For bands with lower and higher SNR, the selected model used two parallel connected detectors.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks

Jeevangi¹,

Jawaligi²,

Patil³

2022

JTIT

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Vacant frequency bands are used in cognitive radio (CR) by incorporating the spectrum sensing (SS) technique. Spectrum sharing plays a central role in ensuring the effectiveness of CR applications. Therefore, a new multi-stage detector for robust signal and spectrum sensing applications is introduced here. Initially, the sampled signal is subjected to SNR estimation by using a convolutional neural network (CNN). Next, the detection strategy is selected in accordance with the predicted SNR levels of the received signal. Energy detector (ED) and singular value-based detector (SVD) are the solutions utilized in the event of high SNR, whilst refined non-negative matrix factorization (MNMF) is employed in the case of low SNR. CNN weights are chosen via the Levy updated sea lion optimization (LU-SLNO) algorithm inspired by the traditional sea lion optimization (SLNO) approach. Finally, the outcomes of the selected detectors are added, offering a precise decision on spectrum tenancy and existence of the signal.

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“…A multistage spectrum sensing is reported in Mourougayane et al 6 which utilizes two parallel detectors for high and low signal to noise ratio (SNR) bands. For high SNR bands, energy detector and singular value‐based detector are used.…”

Section: Introductionmentioning

confidence: 99%

An efficient adaptive threshold‐based dragonfly optimization model for cooperative spectrum sensing in cognitive radio networks

Jothiraj¹,

Sridevi²,

Rangaraj

2021

Int J Communication

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Effective utilization of spectrum resources is an important factor in wireless communication which reduces spectrum scarcity. Over the years, communication systems use different frequency bands, and the users are categorized into licensed and unlicensed users. Most of the wireless bands are typically licensed; as a result, accommodation of new technologies such as Internet of Things and machine to machine communication becomes difficult. So it is essential to obtain a wireless spectrum to adopt new technologies. Cognitive radio technology is introduced to improve such spectrum utilization. Reports state that most of the licensed spectrums are underutilized, and few spectrums are overutilized. Cognitive radio networks help to exploit the licensed spectrum and access the spectrum without any interference to the licensed user.Through its spectrum sensing and spectrum sharing process, cognitive radio network gains more attention in wireless communication. This research work proposed an efficient optimized spectrum sensing technique for cognitive radio networks through dragonfly optimization algorithm along with the adaptive threshold process. Proposed work performs better in terms of detection accuracy and efficiency when compared to conventional spectrum sensing schemes such as linear support vector machine and particle swarm optimization models.

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A robust multistage spectrum sensing model for cognitive radio applications

Cited by 16 publications

References 16 publications

Affirmed Crowd Sensor Selection based Cooperative Spectrum Sensing

Affirmed Crowd Sensor Selection based Cooperative Spectrum Sensing

Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks

An efficient adaptive threshold‐based dragonfly optimization model for cooperative spectrum sensing in cognitive radio networks

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