Performance Analysis of an EH-CRN under Alpha-Mu Fading scenario

Talukdar, Banani; Kumar, Deepak; Kundu, Arnab; Arif, Wasim

doi:10.1109/acts49415.2020.9350426

Cited by 2 publications

(5 citation statements)

References 21 publications

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“…, where : b c describes ceiling function and shows the greatest integer not greater than the parameter, can be obtained using Equations ( 22), (23), and (24). When there is channel error, the overall AER is estimated as…”

Section: Mathematical Framework For Ed-wcrsnmentioning

confidence: 99%

“…where PðnÞ is the Bayes risk function, pðH 1 Þ ¼ pðH 1 Þ ¼ 0:5 is assumed in this paper. When q ¼ 0 is used in ( 18) and ( 19), cooperative probabilities and AER expressions for error-free ED-WCRSN are generated using ( 23), (24), and (25).…”

Section: Mathematical Framework For Ed-wcrsnmentioning

confidence: 99%

“…8. Calculate cooperative detection (Q αÀμ de ), missed detection (Q αÀμ me ), false alarm (Q αÀμ f e ), and AER using ( 22), ( 23), (24), and (25), respectively. 9.…”

Section: Simulation Frameworkmentioning

confidence: 99%

“…In addition to combining numerous different fading models, the

α - μ

fading model typically provides the best fit for the fading envelop with small values for the provided

α

and

μ

values 22,23 . Talukdar et al 24 developed outage probability formula in closed form under

α - μ

fading environment. In Singh and Soni, 25 it is investigated how well EDs perform in the maximal ratio combining (MRC) diversity‐based

α - μ

fading environment using an analytical methodology.…”

Section: Introductionmentioning

confidence: 99%

“…It is necessary to calculate the optimal number of CRSs, that is, n αÀμ opt , which is enough to determine the final decision on the PU status. The exact solution of the optimal n is reported in this section, using the optimal voting rule obtained from ( 23), (24), and (25). An optimal voting rule or optimal value of n for minimizing the AER is calculated by taking the partial derivative of (25) with respect to n and setting the result equal to zero.…”

mentioning

confidence: 99%

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Spectrum sensing performance of wireless cognitive radio sensor network with hard‐decision fusion over generalizedα−μfading channels

Nallagonda¹

2023

Int J Communication

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Summary The analysis of spectrum sensing performance of energy detection (ED)‐based wireless cognitive radio sensor network (ED‐WCRSN) with hard‐decision combining (HDC) is presented in this paper. Particularly, several network parameters are derived to estimate the performance of ED‐WCRSN, considering channel errors, noise, and generalized α−μ fading. In the considered network, first, cognitive radio sensor (CRS) senses a primary user (PU), gets sensing data, and then uses an ED to make a local binary decisions about PU's active or inactive status. In both sensing and reporting channels, the channel error probability is also taken into account. Next, HDC technique is used at control center (CC) to combine the locally obtained decisions, and a final decision about the status of the PU is made. To do so, first, the expression for the novel and analytical, which incorporates noise and α−μ fading, detection probability in a CRS is derived and validated using Monte Carlo simulations in MATLAB and using an experimental setup. Then, utilizing derived mathematical expressions, closed‐form expressions of an average error rate (AER), optimal numbers of CRSs, and detection thresholds under noisy and α−μ fading conditions are developed. The substantial influence of channel and network factors is assessed using receiver operating characteristics (ROC), complementary ROC, and AER. Finally, the impacts of channel and network parameters on ED‐WCRSN performance are explored. For numerous parameters of the considered network, the optimal values detection threshold and number of CRSs are also found.

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Section: Mathematical Framework For Ed-wcrsnmentioning

confidence: 99%

Section: Mathematical Framework For Ed-wcrsnmentioning

confidence: 99%

“…8. Calculate cooperative detection (Q αÀμ de ), missed detection (Q αÀμ me ), false alarm (Q αÀμ f e ), and AER using ( 22), ( 23), (24), and (25), respectively. 9.…”

Section: Simulation Frameworkmentioning

confidence: 99%

“…In addition to combining numerous different fading models, the