On efficacy of Rayleigh‐inverse Gaussian distribution over K‐distribution for wireless fading channels

Karmeshu,; Agrawal, Rajeev

doi:10.1002/wcm.295

Cited by 90 publications

(64 citation statements)

References 10 publications

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“…In this regard and by using the same approach as presented in [17], it was shown that the Nakagami-Inverse lognormal distribution has a closed form given by [20, Equation (5)]:…”

Section: Gg-l Composite Model With V = 1 (N-l)mentioning

confidence: 99%

Performance analysis of lognormally shadowed generalized Gamma fading channels

Samimi

2010

Int J Communication

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SUMMARYA new composite channel model is proposed for the performance analysis of shadowed fading channels. This model is represented as a mixture of generalized Gamma (GG) multipath fading and lognormal shadowing. GG distribution includes the Rayleigh, Nakagami, and Weibull as special cases; hence the presented model, which is referred to as GG-L, is a generic model that covers many well-known composite fading models, including the Rayleigh-lognormal (R-L), Nakagami-lognormal (N-L), and Weibull-lognormal (W-L). The main drawback of the lognormal-based composite models is that the composite probability density function (PDF) is not in closed form, thereby making the performance evaluation of communication links in these channels cumbersome. To bypass this problem, an approximation method is developed which makes it possible to derive a closed-form, analytical expression for GG-L composite distribution. The proposed method only needs the mean and the variance of the underlying lognormal distribution, and hence, bypasses the required complicated integration needed to calculate the PDF of the received signal envelope in GG-L channel. Based on this method, the most statistical characteristics, such as cumulative density function (CDF) and moments of the GG-L composite distribution, are derived and used for the performance analysis of a single receiver operating over GG-L fading channel.

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“…In this regard and by using the same approach as presented in [17], it was shown that the Nakagami-Inverse lognormal distribution has a closed form given by [20, Equation (5)]:…”

Section: Gg-l Composite Model With V = 1 (N-l)mentioning

confidence: 99%

Performance analysis of lognormally shadowed generalized Gamma fading channels

Samimi

2010

Int J Communication

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“…To the best of our knowledge, there exists no closed-form expression for the average probability of detection when we substitute (8) and (10) into (9). The log-normal distribution can be closely approximated by the Wald distribution (also known as the inverse Gaussian distribution) [11], [12], whose PDF is given by…”

Section: Local Energy Detection In a Slow Fading Channelmentioning

confidence: 99%

Computationally Tractable Model of Energy Detection Performance over Slow Fading Channels

2010

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Abstract-Energy detection (ED) has been widely used fordetecting unknown deterministic signals in many wireless communication applications, e.g., cognitive radio, and ultra-wideband (UWB). However, the performance analysis of ED over slow fading channels is cumbersome, because it is difficult to derive closed-form expressions for the average probability of detection involving the generalised Marcum Q-function and the log-normal distribution. In this letter, we derive an approximation of the average probability of detection over a slow fading channel by replacing the log-normal distribution with a Wald distribution. In addition, we analyze the detection performance of the ED using a square-law combining scheme over multiple independent and identically distributed slow fading channels.

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“…Following this, in many works focused on databit level analyses, gamma mixture distribution is utilized to characterize the shadow fading in composite models which include macroscopic and microscopic fadings together [8], [9]. Various other shadow fading models such as inverse Gaussian have also been considered in such composite channels [10].…”

Section: Introductionmentioning

confidence: 99%

An empirical study on gamma shadow fading based localization

Büyükçorak

Kurt

Yongaçoğlu

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, we propose a maximum likelihood estimator for received signal strength (RSS) based indoor localization systems by exploiting gamma shadow fading model. In order to investigate the validity of proposed method in a realistic environment, we develop a testbed based on Wi-Fi technology. Through experimental analyses, we first demonstrate the gamma distribution is a good fit to lognormal distribution, and both of them can sufficiently accurately characterize the empirical RSS observations. Then, we observe that gamma distribution is worth investigating for indoor localization compared to lognormal model because it provides superior accuracy. We further analyze the impacts of uncertainties of considered distributions' parameters on the localization performance via simulations.

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On efficacy of Rayleigh‐inverse Gaussian distribution over K‐distribution for wireless fading channels

Cited by 90 publications

References 10 publications

Performance analysis of lognormally shadowed generalized Gamma fading channels

Performance analysis of lognormally shadowed generalized Gamma fading channels

Computationally Tractable Model of Energy Detection Performance over Slow Fading Channels

An empirical study on gamma shadow fading based localization

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