Closed form expression for the inverse cumulative distribution function of Nakagami distribution

Quantile function or inverse cumulative distribution function (CDF) is heavily utilized in modelling, simulation, reliability analysis and random number generation. The use is often limited if the inversion method fails to estimate it from the cumulative distribution function. As a result, approximation becomes the other feasible option. The failure of the inversion method is often due to the intractable nature of the CDF of the distribution. Approximation may come in the form of series expansions, closed form or functional approximation, numerical algorithm and the closed form expression drafted in terms of the quantile function of another distribution. This work used the cubic spline interpolation to obtain the closed form of the inverse cumulative distribution function of the Nakagami-m distribution. Consequently, the closed form of the quantile function obtained for the selected parameters of the distribution serves as an approximation which compares favourably with the R software values. The result obtained was a significant improvement over some results surveyed from literature for four reasons. Firstly, the approximates produced better results in simulation as evidenced by the some values of the root mean square error of this work when compared with others. Secondly, the result obtained at the extreme tail of the distribution is better than others selected from the literature. Thirdly, the closed form estimates are easy to compute and save computation time. The closed form of the quantile function obtained in this work can be used in simulating Nakagami random variables which are used in modelling attenuation and fading channels in wireless communications and ultrasonic tissue characterization.

[1]  A. Mishra,et al.  Maximum likelihood estimate of parameters of Nakagami-m distribution , 2012, 2012 International Conference on Communications, Devices and Intelligent Systems (CODIS).

[2]  Dazhuan Xu,et al.  Highly efficient rejection method for generating Nakagami-m sequences , 2011 .

[3]  Tung-Sang Ng,et al.  A simulation model for Nakagami-m fading channels, m<1 , 2000, IEEE Trans. Commun..

[4]  P. Shankar Ultrasonic tissue characterization using a generalized Nakagami model , 2001, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[5]  Norman C. Beaulieu,et al.  Efficient Nakagami-m fading channel Simulation , 2005, IEEE Transactions on Vehicular Technology.

[6]  Yasin Kabalci On the Nakagami-m Inverse Cumulative Distribution Function: Closed-Form Expression and Its Optimization by Backtracking Search Optimization Algorithm , 2016, Wirel. Pers. Commun..

[7]  M.D. Yacoub,et al.  The $\alpha$-$\mu$ Distribution: A Physical Fading Model for the Stacy Distribution , 2007, IEEE Transactions on Vehicular Technology.

[8]  M. Yacoub,et al.  On higher order statistics of the Nakagami-m distribution , 1999 .

[9]  Hilary I. Okagbue,et al.  Ordinary differential equations of probability functions of convoluted distributions , 2018, International Journal of ADVANCED AND APPLIED SCIENCES.

[10]  Gábor Jeney,et al.  Coverage analysis for macro users in two-tier Rician faded LTE/small-cell networks , 2015, Wirel. Networks.

[11]  Yik-Chung Wu,et al.  Exact Outage Probability of Dual-Hop CSI-Assisted AF Relaying Over Nakagami-$m$ Fading Channels , 2012, IEEE Transactions on Signal Processing.

[12]  Zhiyong Feng,et al.  Simultaneous wireless information and power transfer for relay assisted energy harvesting network , 2016, Wireless Networks.

[13]  Yuefeng Ji,et al.  Collision analysis of CSMA/CA based MAC protocol for duty cycled WBANs , 2017, Wirel. Networks.

[14]  Aditya Trivedi,et al.  Outage and energy efficiency analysis for cognitive based heterogeneous cellular networks , 2016, Wireless Networks.

[15]  Francisco Louzada,et al.  The Inverse Nakagami-m Distribution: A Novel Approach in Reliability , 2018, IEEE Transactions on Reliability.

[16]  Radovan Jirik,et al.  Estimator Comparison of the Nakagami-m Parameter and its Application in Echocardiography , 2004 .

[17]  M. Nakagami The m-Distribution—A General Formula of Intensity Distribution of Rapid Fading , 1960 .

[18]  David Irvine Laurensen Indoor radio channel propagation modelling by ray tracing techniques , 1994 .

[19]  M. Marsden,et al.  Cubic spline interpolation of continuous functions , 1974 .

[20]  Susmita Das,et al.  Reliable communication in UWB body area networks using multiple hybrid relays , 2017, Wirel. Networks.

[21]  Asad Munir,et al.  Dependency without copulas or ellipticity , 2009 .

[22]  Yasin Kabalci,et al.  An improved approximation for the Nakagami-m inverse CDF using artificial bee colony optimization , 2018, Wirel. Networks.

[23]  Norman C. Beaulieu,et al.  Maximum-likelihood based estimation of the Nakagami m parameter , 2001, IEEE Communications Letters.

[24]  Cecil Hastings,et al.  Approximations for digital computers , 1955 .

[25]  Lei Shi,et al.  A New Statistical WRELAX Algorithm Under Nakagami Multipath Channel Based on Delay Power Spectrum Characteristic , 2015, Wirel. Pers. Commun..

[26]  Farhad Bahadori-Jahromi,et al.  Joint design of physical and MAC layer by applying the constellation rearrangement technique in cooperative multi-hop networks , 2017, Wirel. Networks.

[27]  Yan Gao,et al.  Channel estimation for AF relaying using ML and MAP , 2017, Wireless Networks.

[28]  Ghanshyam Singh,et al.  Channel capacity in fading environment with CSI and interference power constraints for cognitive radio communication system , 2014, Wireless Networks.

[29]  Yao Ma,et al.  A Method for Simulating Complex Nakagami Fading Time Series With Nonuniform Phase and Prescribed Autocorrelation Characteristics , 2010, IEEE Transactions on Vehicular Technology.

[30]  B. Goldberg,et al.  Classification of ultrasonic B-mode images of breast masses using Nakagami distribution , 2001, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[31]  Okagbue et al.,et al.  Quantile mechanics: Issues arising from critical review , 2019, International Journal of ADVANCED AND APPLIED SCIENCES.

[32]  R. Hughey,et al.  A survey and comparison of methods for estimating extreme right tail-area quantiles , 1991 .

[33]  Norman C. Beaulieu,et al.  Simple Efficient Methods for Generating Independent and Bivariate Nakagami- $m$ Fading Envelope Samples , 2007, IEEE Transactions on Vehicular Technology.

[34]  Do Le Minh,et al.  A New Fixed Point Iteration to Find Percentage Points for Distributions on the Positive Axis , 2010 .

[35]  A. McNeil,et al.  Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach , 2000 .

[36]  Luca Martino,et al.  Almost rejectionless sampling from Nakagami-m distributions (m⩾1) , 2012 .

[37]  On the Beta-Nakagami Distribution , 2013 .

[38]  Mehmet Bilim,et al.  A New Nakagami-m Inverse CDF Approximation Based on the Use of Genetic Algorithm , 2015, Wirel. Pers. Commun..

[39]  Matthias Patzold,et al.  Mobile Fading Channels: Modelling,Analysis and Simulation , 2001 .

[40]  Hilary I. Okagbue,et al.  Closed Form Expressions for the Quantile Function of the Erlang Distribution Used in Engineering Models , 2019, Wirel. Pers. Commun..

[41]  Ghanshyam Singh,et al.  Capacity in fading environment based on soft sensing information under spectrum sharing constraints , 2017, Wirel. Networks.

[42]  Ümit Aygölü,et al.  Performance analysis of a multihop relay network using distributed Alamouti code , 2015, Wirel. Networks.