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Chauhan, Puspraj Singh; Negi, Priyanka; Soni, Sanjay Kumar

doi:10.1016/j.aeue.2018.01.035

Cited by 22 publications

(21 citation statements)

References 26 publications

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“…As stated in Chauhan et al, the hypergeometric and the Bessel's K function have inverse relationship with respect to “ m. ” The former decreases with the increasing value of m , while the latter increases. But for a fitting integer, say “ m 0 ” such that for m > m 0 , the product of both the functions declines monotonically.…”

Section: Performance Metrics Of An Energy Detectormentioning

confidence: 89%

“…For sensing of the spectrum, there are many approaches available; among them, the energy detection (ED) technique is widely used. The ED does not prerequisite any former information of the signal, which is transmitted by PU and also has low computational complexity and cost . It basically works by simply relating the energy levels of the received signal to a predefined threshold value and then makes its judgment on whether the spectrum is available for communication by the SU or not.…”

Section: Introductionmentioning

confidence: 99%

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Development of analytical model for energy detectors with SC and MRC diversity over Inverse‐Gamma fading

Maurya

Srivastava

Chauhan

et al. 2020

Int J Communication

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Summary An ever‐escalating demand for wireless applications has caused great concern for the proper exploitation of the accessible radio spectrum. Cognitive radio materializes as an auspicious remedy to the present‐day crisis of spectral congestion, by detecting the licensed primary user (PU). This is accomplished with the assistance of the spectrum sensing technique, which provides an indication of the presence of PU over the spectrum. Energy detection is one of the prevailing spectrum sensing techniques due to its low implementation complexity. In the present work, the performance of an energy detector (ED) over Inverse‐Gamma (I‐Gamma) fading distribution is examined. Initially, a closed‐form expression of the probability density function for I‐Gamma distribution with maximal ratio combining diversity reception is derived. Following it, an investigation of an ED‐based cognitive radio device is carried out in the form of the average probability of detection (PD) and average area under the receiver operating characteristic curve (AUC). In addition, we also present a performance analysis of an ED with selection combining diversity. Optimization of the detection threshold is also executed alongside the low signal‐to‐noise ratio analysis. In the end, the resulting expression of the PD is exploited to examine the functioning of cooperative spectrum sensing within the erroneous environment. The validation of derived mathematical forms has been confirmed by comparing it with the Monte‐Carlo simulation and exact numerical results.

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Section: Performance Metrics Of An Energy Detectormentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Development of analytical model for energy detectors with SC and MRC diversity over Inverse‐Gamma fading

Maurya

Srivastava

Chauhan

et al. 2020

Int J Communication

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“…Threshold optimization is important for minimizing the probabilities of false alarm and missed detection, and therefore, improving the performance of the CR . This is achieved by minimizing the probability of error P e = (1 − P d ) + P f through partial differentiation followed by equation to zero

\frac{\partial P_{e}}{\partial λ} = \frac{\partial P_{f}}{\partial λ} - \frac{\partial P_{d}}{\partial λ} = 0

and then solving for λ .…”

Section: Average Probability Of Detectionmentioning

confidence: 99%

“…For the i th CR equipped with ED, the probability of false detection, probability of detection, and the probability of missed detection over AWGN channels are given respectively as

P_{f, i} = normalΓ ((), u, \frac{λ_{i}}{2}) false/ normalΓ false(u false),

P_{d, i} = Q_{u} ((), \sqrt{2 γ_{i}}, \sqrt{λ_{i}}),

P_{m, i} = 1 - P_{d, i} .

After the energy detection has been performed, each SU takes a binary decision based on its findings and then sends one bit of the decision D i (0 for the absence of the PU and 1 for the presence of the PU) to the FC via an error‐free channel. At the FC, all 1‐bit decisions are gathered together according to n ‐ out ‐ of ‐ K voting rule . As a result, the final decision statistic Y can be written as

Y = true \sum_{i = 1}^{K} D_{i} ({, \begin{matrix} center \\ array < n, & array H_{0} \\ array \geq n, & array H_{1} . \end{matrix})

It is further assumed that all the SUs operate under i.i.d channel effects with the same mean, ie,

{\overset{γ}{true}}_{1} = {\overset{γ}{true}}_{2} = ⋯ = {\overset{γ}{true}}_{K} = \overset{γ}{true}

.…”

Section: Average Probability Of Detectionmentioning

confidence: 99%

An integrated performance evaluation of ED‐based spectrum sensing over α−κ−μ and α−κ−μ‐Extreme fading channels

Rasethuntsa

Kumar

2019

Trans Emerging Tel Tech

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This paper presents a comparative performance evaluation of energy detector (ED) under the assumption that the propagation signal experiences fading modeled by the nonlinear generalized α−κ−μ or α−κ−μ‐Extreme fading channels. Novel closed‐form analytical expressions for the average probability of detection and the average area under the receiver operating characteristic (ROC) curve (AUC) are derived in terms of the now highly prevalent bivariate Fox H‐function under single‐user spectrum sensing, cooperative spectrum sensing, and square law selection diversity reception. The results derived in the present work are generic as both channel models are compound distributions containing as special cases several other well‐known fading models such as the Nakagami‐m, Rice, the one‐sided Gaussian distribution, and the Weibull distribution, among others. The performance of ED is evaluated by employing the traditional ROC and AUC curves over additive white Gaussian noise by varying model parameters and observing system performances. Analytical results are verified via Monte Carlo simulations in MATLAB®.

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“…26 In spite of its closeness to LN, it is difficult to obtain the exact closed-form solution for the optimal rate adaptation (ORA) capacity and selection combining (SC) probability density function (PDF) or simplified representation of the metrics. 31 Therefore, researchers presented a new model known as inverse gamma (I-Gamma) as a substitute that lies in between gamma and IG, such that it facilitates evaluating the closed-form solutions of the composite PDF. [26][27][28] But the composite structure with more generalised multipath fading such as − /IG, − /IG, and − /IG are presented either in finite/infinite series expansions 29,30 or as sum of the mixture of IG distributions.…”

Section: Introductionmentioning

confidence: 99%

Error probability and channel capacity analysis of wireless system over inverse gamma shadowed fading channel with selection diversity

Pant

Chauhan

Soni

2019

Int J Communication

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In this paper, we present an analytical framework to analyse the error probability and the channel capacity of the inverse gamma (I-Gamma) shadowed fading channel. First, the work discusses the utility of the I-Gamma over log-normal (LN) and gamma fading models where the closeness of I-Gamma with other existing shadowing models is carried out. Utilising the probability density function (PDF) of the I-Gamma shadowed fading channel, various metrics of the communication system, namely, the average symbol error probability (SEP), the channel capacity under optimal rate adaptation (ORA), channel inversion with fixed rate (CIFR), and truncated CIFR (TIFR) are derived. Further, the work is extended to derive a novel selection combining (SC) PDF, and the analytical results for the SEP and the channel capacity of SC diversity are presented. Furthermore, we also provide simpler asymptotic expressions for the average SEP. In addition, the simplified high and low signal-to-noise-ratio (SNR) solutions to channel capacity are also provided. The derived mathematical formulations have been endorsed by comparing with Monte Carlo simulations. KEYWORDSBessel K, bit error rate, capacity, distribution, fading Int J Commun Syst. 2019;32:e4083.wileyonlinelibrary.com/journal/dac

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A unified approach to modelling of probability of detection overα-μ/IG,κ-

Cited by 22 publications

References 26 publications

Development of analytical model for energy detectors with SC and MRC diversity over Inverse‐Gamma fading

Development of analytical model for energy detectors with SC and MRC diversity over Inverse‐Gamma fading

An integrated performance evaluation of ED‐based spectrum sensing over α−κ−μ and α−κ−μ‐Extreme fading channels

Error probability and channel capacity analysis of wireless system over inverse gamma shadowed fading channel with selection diversity

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