An optimal and adaptive double threshold-based approach to minimize error probability for spectrum sensing at low SNR regime

Mahendru, Garima; Shukla, Anil Kumar; Patnaik, L. M.

doi:10.1007/s12652-021-03596-w

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…In contrast, TCTDT-ED methods require SNR values of -10 dB to achieve a similar level of detection probability. The conventional method using ED such as adaptive double threshold-ED (ADT-ED) [27] requires an SNR of more than -2dB before their detection probability approaches 1.The HBAT-ED also performs same as proposed algorithm and diminishes as the SNR value rises. In Figure 7, the depiction illustrates available time slots for SUs to occupy the PU.…”

Section: B Cramer-von Mises(cm) Sensing Methodsmentioning

confidence: 96%

Analysis of Wideband spectrum sensing using Anderson darling test statistics in underlay cognitive microcell

G. Kalaimagal

2024

jes

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The most widely used sensing technique for detecting energy is due to its ease of use and lack of need for signal information beforehand. Traditional methods often employ a two-threshold strategy in low signal-to-noise ratio scenarios, which can lead to temporary detection outcomes when the signal's energy is between the low and high threshold values. This subsequently results in inadequate detection accuracy, characterized by a reduced probability of detection, which ultimately results in an extended duration of the spectrum sensing. The paper suggests enhancing the efficiency of spectrum sensing through the utilization of a modified version of the Anderson-Darling test statistic, as opposed to other test statistics.To detect the presence of licensed users in each subchannel from the base station, the cognitive user implements the Anderson Darling test statistics as a sensing technique. The proposed work involves the mathematical derivation of the Anderson-Darling test statistics under a bandwidth of 6MHz.The simulation outcomes show that the sensing technique presented in this research study achieved a detection probability of 0.963 at a false alarm probability of 1%, using a small sample size of 20 at a signal-to-noise ratio of -20dB. This performance outperformed the other three detection algorithms.

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Section: B Cramer-von Mises(cm) Sensing Methodsmentioning

confidence: 96%

Analysis of Wideband spectrum sensing using Anderson darling test statistics in underlay cognitive microcell

G. Kalaimagal

2024

jes

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“…Spectrum sensing (SS) is performed by secondary user's access frequency spectrum dynamically. In order to establish a communication link for the Secondary User (SU) without interfering with the Primary User's (PU) transmission, SS is a crucial component of a CR network that requires the detection of an open spectrum space [4,5]. The three primary approaches for determining the signal presence each have some trade-offs.…”

Section: Introductionmentioning

confidence: 99%

Efficient Cyclostationary Spectrum Sensing Using Low Complexity FFT Algorithms

H. Abood,

Hikmat N. Abdullah

2024

Iraqi Journal of ICT

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One of the major problems of cyclostationary spectrum sensing (CSS) system in cognitive radios (CR) are the implementation complexity. One possible way to reduce CSS complexity is to use efficient algorithms for performing Fast Fourier Transformation (FFT). Over the years, a lot of different FFT algorithms have been created. This includes the Split-Radix algorithm., the Fast Hartley Transform (FHT), and slide DFT. This paper investigates the suitable FFT algorithm among the aforementioned techniques, cyclostationary feature detection (CFD)-based spectrum sensing stands out. The methods have been thoroughly compared based on computational time, object size, code size, data dependence (real or complex), and the amount of mathematical operations involved in the computations. Simulation results show that slide FFT is the suitable frequency domain transformation algorithm to use in implementing cyclostationary spectrum sensing in cognitive radios as compared to the other considered algorithms where it provides a significant reduction in FFT stage computation complexity reach to 17% in SRFFT , 78% in FHT and 82% in SDFT while keeping the detection probability at satisfactory level.

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“…Through these sensing methods, the most commonly used technique is energy detection; because it is low cost and simple implementation [2]. The performance of energy detection is deteriorating due to the impact of noise uncertainty, especially in low signal-to-noise ratio SNR environments, in this case, to get a reliable detection the gathered samples for the energy detection test must be large enough [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

Elfatih¹,

Ali²,

Abdelhaq³

et al. 2023

Computer Systems Science and Engineering

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In cognitive radio networks (CoR), the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability. Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection. However, these methods do not take into account the effect of sample size and its effect on improving CoR performance. In general, a large sample size results in more reliable detection, but takes longer sensing time and increases complexity. Thus, the locally sensed sample size is an optimization problem. Therefore, optimizing the local sample size for each cognitive user helps to improve CoR performance. In this study, two new methods are proposed to find the optimum sample size to achieve objective-based improved (single/double) threshold energy detection, these methods are the optimum sample size N* and neural networks (NN) optimization. Through the evaluation, it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate, detection probability, and throughput.

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An optimal and adaptive double threshold-based approach to minimize error probability for spectrum sensing at low SNR regime

Cited by 6 publications

References 29 publications

Analysis of Wideband spectrum sensing using Anderson darling test statistics in underlay cognitive microcell

Analysis of Wideband spectrum sensing using Anderson darling test statistics in underlay cognitive microcell

Efficient Cyclostationary Spectrum Sensing Using Low Complexity FFT Algorithms

A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

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