Performance optimisation of cooperative spectrum sensing in mobile cognitive radio networks

Wu, Jun; Yu, Yue; Song, Tiecheng; Hu, Jing

doi:10.1049/iet-com.2019.1083

Cited by 21 publications

(10 citation statements)

References 27 publications

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“…Another study by (Wu et al 2020) Ramesh et al 2018;Mathew et al 2020;Sridharan et al 2019;Pc, Marimuthu, and Devadoss 2018;Ramadurai et al 2019). We hope this study adds to this rich legacy.…”

Section: Discussionmentioning

confidence: 65%

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Effective Channel Detection at Low SNR in Cognitive Radio Network Using Matched Filter Approach and Compare with Energy Detection - based Approach

Tejesh¹

2021

revistageintec

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Aim: The study aims to detect the effective channel in cognitive radio network at low SNR using an innovative algorithm based on matched filter detection and compared it with energy detection. Materials and methods: The spectrum sensing based on novel matched filter detection with 10 samples is compared with the energy detection by varying the SNR conditions, using MATLAB. Results: The probability of detection of the matched filter is high at low SNR(-30db) then compared to the probability of detection of energy detection at low SNR(-10db) and the significance level is 0.002, i.e., (P<0.05) which gives better sensing performance. Conclusion: this would be proved to conclude that at low SNR conditions the matched filter detection gives significantly high sensing probability.

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Section: Discussionmentioning

confidence: 65%

“…There are various applications where cognitive radio networks are used as mobile computing and all emergency fields like medicine, military, etc. (Yanbin, Zhongji, and Xu 2012); (Wu et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Effective Channel Detection at Low SNR in Cognitive Radio Network Using Matched Filter Approach and Compare with Energy Detection - based Approach

Tejesh¹

2021

revistageintec

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“…Among these LSS technologies, the energy detection is commonly adopted because of its low complexity and also it does not require any prior knowledge of the PU signal. In the energy detection, the PU is usually formulated as a binary hypothesis test problem [25], then the LSS model for the ith mini-slot is described as follows:…”

Section: Lss Performancementioning

confidence: 99%

Optimisation of virtual cooperative spectrum sensing for UAV‐based interweave cognitive radio system

Chen

et al. 2021

IET Communications

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In an interweave cognitive radio system, cooperative spectrum sensing has been recognised as a key technology to enable secondary users to opportunistically access licensed spectrum band without harmful interference to primary users. At the same time, the unmanned aerial vehicle equipped with spectrum sensing and data transmission facilities is gaining more popularity in different applications. An unmanned aerial vehicle-based interweave cognitive radio is investigated in which the unmanned aerial vehicle is used as a secondary user, but unlike the participation of multiple secondary users in traditional cooperative spectrum sensing, a virtual cooperative spectrum sensing model is introduced into the periodic spectrum sensing frame structure. Afterwards, the authors further propose an energy-efficient virtual cooperative spectrum sensing with the sequential 0/1 fusion rule to reduce the average number of decisions without any loss in the detection performance. Sequentially, the authors formulate the optimisation of virtual cooperative spectrum sensing for unmanned aerial vehicle-based interweave cognitive ratio system as the optimal sequential 0/1 fusion problem on the basis of the K-out-of-N fusion rule and prove the formulated problem indeed has one optimal K, which yields the highest throughput. Finally, numerical simulations are presented to demonstrate the correctness of theoretical analyses and the effectiveness of the virtual cooperative spectrum sensing with the sequential 0/1 fusion rule.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Instead of considering only the received signal power for detection an additional optimal exponent varying in [0, 2] is used for observing the detection and false alarm probability and the result showed that the proposed i ‐ED outperformed ED. The analysis of miss‐detection and false alarm probability based on user characteristics in dynamic double threshold energy detection (DDTED) scheme of CRN are presented in Reference 29. The analysis of user characteristics on the performance of cooperative spectrum sensing depicts that the performance of the cooperative CRN is highly influenced by the user characteristics variation.…”

Section: Introductionmentioning

confidence: 99%

Optimization of secondary user capacity in a centralized cooperative cognitive radio network with primary user under priority

Debnath

Rai

Baishya

et al. 2020

Engineering Reports

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The primary objective of cognitive radio is to maximize the spectrum utilization and the capacity of secondary users under standard parametric constraints. The radiometer, or energy detection, is one of the convenient methods of spectrum sensing under a cooperative communication framework. However, the selection of a suitable threshold is a major issue in order to optimize the secondary user capacity considering primary user on priority. In this article, we investigate a double threshold energy detection scheme and obtain an analytical model for optimum thresholding in diverse fading conditions under the constraint of the probability of detection. We use a meta-heuristic optimization algorithm, namely, the particle swarm optimization with an aging leader and challengers, to observe the effect of varying threshold levels in fusion region, signal to noise ratio (SNR) levels of primary user, and calculate the normalized achievable capacity of secondary user for various fading channels. The results show that, with increasing value of threshold within the decision region, the probability of detection decreases while the normalized secondary user capacity is enhanced. Conversely, the probability of detection increases and the normalized secondary user capacity decreases with increasing values of the SNR level. We also observe the effect of receiver diversity with maximal ratio combining for Nakagami-m fading channel coefficients. The result shows that the probability of detection and normalized capacity decrease with an increase of the number of receiver branches.

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Performance optimisation of cooperative spectrum sensing in mobile cognitive radio networks

Cited by 21 publications

References 27 publications

Effective Channel Detection at Low SNR in Cognitive Radio Network Using Matched Filter Approach and Compare with Energy Detection - based Approach

Effective Channel Detection at Low SNR in Cognitive Radio Network Using Matched Filter Approach and Compare with Energy Detection - based Approach

Optimisation of virtual cooperative spectrum sensing for UAV‐based interweave cognitive radio system

Optimization of secondary user capacity in a centralized cooperative cognitive radio network with primary user under priority

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