An Improved Cyclostationary Feature Detection Algorithm

Yan, Tianfeng; Xu, Fuxin; Wu, Nan; Yang, Zhifei

doi:10.1007/978-3-030-14680-1_60

“…Over two decades, researchers worldwide explored upon various methods for spectrum sensing. Based on the literature survey on spectrum sensing various methods like: Energy detection (ED) [8][9][10][11]; Cyclo-stationary feature detection [12][13][14]; Matched filter detection [15,16]; Statistical covariance-based detection [17,18]; Cooperative sensing [19,20]; compressed sensing [21][22][23] were proposed to examine the spectrum occupancy under different scenarios. Considering the research on spectrum sensing methods; energy detection draws the attention of the researchers for its lesser computational and operational complexity [24].…”

Section: Related Workunclassified

A Novel Double Threshold-Based Spectrum Sensing Technique at Low SNR Under Noise Uncertainty for Cognitive Radio Systems

Mahendru

¹

2022

Wireless Pers Commun

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Cognitive Radio is a novel concept that has invoked a paradigm shift in wireless communication and promises to solve the problem of spectrum underutilization. Spectrum sensing plays a pivotal role in a cognitive radio system by detecting the vacant spectrum for establishing a communication link. For any spectrum sensing method, detection probability and error probability portray a significant part in quantifying the detection performance. At low SNR, it becomes cumbersome to differentiate noise and signal due to which sensing method loses robustness and reliability. In this paper, mathematical modeling and critical measurement of detection probabilities has been done for energy detection-based spectrum sensing at low SNR in uncertain noisy environment. A mathematical model has been proposed to compute double thresholds for reliable sensing when the observed energy is less than the uncertainty in the noise power. A novel parameter “Threshold Wall” has been formulated for optimum threshold selection to overcome sensing failure. Comparative simulation and analytical result measurements have been presented that reveals improved sensing performance.Please check inserted city is correct for affiliation 1.Noida, it is correct

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“…In order to reduce computation complexity while maintaining sufficient detection sensitivity, authors of [79] proposed an improved Cyclostationary Detector with SLC Diversity over Nakagami-m Fading channels, where the test statistic of conventional Cyclostationary detector is reliably simplified. For the same purpose, in [80], an improved Cyclostationary Feature Detection Algorithm is presented, in which authors proved that the cyclic spectrum is conjugate symmetry about the relevant axis, which decreases the computational complexity.…”

Section: C) Cyclostationary Feature Detection: Cyclostationarymentioning

confidence: 99%

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

Kassri¹,

En‐Nouaary²,

Bah³

et al. 2021

IJACSA

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The inherent scarcity of frequency spectrum, along with the fixed spectrum allocation adopted policy, has led to a dire shortage of this indispensable resource. Furthermore, with the tremendous growth of wireless applications, this problem is intensified as the unlicensed frequency spectrum becomes overcrowded and unable to meet the requirement of emerging radio devices operating at higher data rates. Additionally, the already assigned spectrum is underutilized. That has prompted researchers to look for a way to address spectrum scarcity and enable efficient use of the available spectrum. In this context, Cognitive Radio (CR) technology has been proposed as a potential means to overcome this issue by introducing opportunistic usage to less congested portions of the licensed spectrum. In addition to outlining the fundamentals of Cognitive Radio, including Dynamic Spectrum Access (DSA) paradigms and CR functions, this paper has a three-fold objective: first, providing an overview of Software Defined Radio (SDR), in which the architecture, benefits, and ongoing challenges of SDR are presented; second, giving an extensive review of spectrum sensing, covering sensing types, narrowband and wideband sensing schemes with their pros and cons, Machine Learningbased sensing, and open issues that need to be further addressed in this field; third, exploring the use of Cognitive Radio in the Internet of Things (IoT) while highlighting the crucial contribution of CR in enabling IoT. This Review is elaborated in an informative fashion to help new researchers entering the area of Cognitive Radio Networks (CRN) to easily get involved.

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A novel double threshold-based spectrum sensing technique at low SNR under noise uncertainty for Cognitive Radio Systems

Mahendru¹

2021

Preprint

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Cognitive Radio is a novel concept that has invoked a paradigm shift in wireless communication and promises to solve the problem of spectrum underutilization. Spectrum sensing plays a pivotal role in a cognitive radio system by detecting the vacant spectrum for establishing a communication link. For any spectrum sensing method, detection probability and error probability portray a significant part in quantifying the detection performance. At low SNR, it becomes cumbersome to differentiate noise and signal due to which sensing method loses robustness and reliability. In this paper, mathematical modeling and critical measurement of detection probabilities has been done for energy detection-based spectrum sensing at low SNR in uncertain noisy environment. A mathematical model has been proposed to compute double thresholds for reliable sensing when the observed energy is less than the uncertainty in the noise power. A novel parameter “Threshold Wall” has been formulated for optimum threshold selection to overcome sensing failure. Comparative simulation and analytical result measurements have been presented that reveals improved sensing performance.

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An Improved Cyclostationary Feature Detection Algorithm

Cited by 3 publications

References 3 publications

A Novel Double Threshold-Based Spectrum Sensing Technique at Low SNR Under Noise Uncertainty for Cognitive Radio Systems

A Novel Double Threshold-Based Spectrum Sensing Technique at Low SNR Under Noise Uncertainty for Cognitive Radio Systems

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

A novel double threshold-based spectrum sensing technique at low SNR under noise uncertainty for Cognitive Radio Systems

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