The <i>κ-μ</i> / Inverse Gamma and <i>η-μ</i> / Inverse Gamma Composite Fading Models: Fundamental Statistics and Empirical Validation

Yoo, Seong Ki; Simmons, David E.; Cotton, Simon L.; Sofotasios, Paschalis C.; Matthaiou, Michail; Valkama, Mikko; Karagiannidis, George K.

doi:10.1109/tcomm.2017.2780110

Cited by 70 publications

(91 citation statements)

References 35 publications

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“…Similarly, we can also find the number of terms required to achieve the accuracy for all derived infinite series expressions. Figure 3 represents the plot of average BER for BPSK modulation compared with ABER (BPSK) using PDF-based approach (see (11) in the work of Shankar and Kansal 32 ). We have used various κ, μ, α, and β parameters to evaluate the result of derived expression presented in (14).…”

Section: Resultsmentioning

confidence: 99%

MGF‐based analysis of κ‐μ/gamma composite fading model for indoor off body communication

Shankar

Kansal

2019

Trans Emerging Tel Tech

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This paper presents the moment generating function (MGF)–based analysis of κ‐μ/gamma composite fading model, which is suitable for the indoor off‐body communication channel. In this paper, first of all, we derive an analytical expression for MGF of instantaneous signal‐to‐noise ratio. By using the derived MGF, the expressions of average symbol error rate (ASER)/average bit error rate (ABER) for binary and M‐ary modulation scheme are derived. Using the MGF approach, we also derive the expression of channel capacity for optimum rate adaption and channel inversion with fixed rate policy. The derived expressions for ABER/ASER and channel capacity are presented in the infinite series form. We further present the numerical results of error rate and channel capacity for different values of κ, μ, α, and β parameters. The result of ABER and channel capacity shows good agreement with the previously reported results as special cases that verify the derived expressions.

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Section: Resultsmentioning

confidence: 99%

MGF‐based analysis of κ‐μ/gamma composite fading model for indoor off body communication

Shankar

Kansal

2019

Trans Emerging Tel Tech

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“…Equations (33) and (34) help in attaining the system behaviour in terms of the fading parameters and SNR. For example, the capacity improves with increasing scale parameter and SNR and deteriorates as shape parameter k increases.…”

Section: Optimum Rate Adaptationmentioning

confidence: 99%

“…Subsequently, Figure 8 shows the comparison of ORA (34) and CIFR (39) schemes for low SNR scenario with varying fading parameters. Subsequently, Figure 8 shows the comparison of ORA (34) and CIFR (39) schemes for low SNR scenario with varying fading parameters.…”

Section: Figurementioning

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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“…The I‐Gamma distribution, unlike the IG and Gamma distributions, is less complex, and its PDF follows the long tail characteristic of lognormal . Also, its expression is particularly suitable for the derivation of tractable closed‐form solution, which is further suitable for deriving various performance measures of interest …”

Section: Introductionmentioning

confidence: 99%

“…25,26 Also, its expression is particularly suitable for the derivation of tractable closed-form solution, which is further suitable for deriving various performance measures of interest. [27][28][29] The greed towards high data rate has been increased in the last few years. The technology has developed and converted from voice to multimedia transmission, where cellular traffic has increased multiple times.…”

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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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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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The κ-μ / Inverse Gamma and η-μ / Inverse Gamma Composite Fading Models: Fundamental Statistics and Empirical Validation

Cited by 70 publications

References 35 publications

MGF‐based analysis of κ‐μ/gamma composite fading model for indoor off body communication

MGF‐based analysis of κ‐μ/gamma composite fading model for indoor off body communication

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

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

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