On the first- and second-order statistical properties of different Rayleigh fading channel models

This paper is a continuation of our previous works on performance of different simulation models for Rayleigh fading channels. Previously, we have compared and analyzed several Rayleigh fading simulation models in terms accuracy and complexity. Some of these models are non-ergodic and their statistical properties vary from one simulation trial to another. Thus, a focus on the convergence behavior of such models is highly recommended which is missing in our previous works. In this paper, the first- and second-order statistical properties of seven of the most popular Rayleigh fading models are reviewed. The quality of the envelope probability density function (PDF) of the different models is evaluated using a more convenient quantitative measure. The convergence of the non-ergodic stochastic models is inspected based on the variances of the time-averaged correlation properties. The simulation results reveal several important conclusions about the accuracy as well as the capacity of the different models.

[1]  Matthias Pätzold Mobile Radio Channels: Pätzold/Mobile Radio Channels , 2011 .

[2]  R. Clarke A statistical theory of mobile-radio reception , 1968 .

[3]  Yahong Rosa Zheng,et al.  Improved models for the generation of multiple uncorrelated Rayleigh fading waveforms , 2002, IEEE Communications Letters.

[4]  Gordon L. Stuber,et al.  Principles of Mobile Communication , 1996 .

[5]  Norman C. Beaulieu,et al.  The generation of correlated Rayleigh random variates by inverse discrete Fourier transform , 2000, IEEE Trans. Commun..

[6]  Kareem E. Baddour,et al.  Autoregressive modeling for fading channel simulation , 2005, IEEE Transactions on Wireless Communications.

[7]  Mongi Lahiani,et al.  A multi-criteria comparative analysis of different Rayleigh fading channel simulators , 2014 .

[8]  Elena Deza,et al.  Encyclopedia of Distances , 2014 .

[9]  Norman C. Beaulieu,et al.  Power margin quality measures for correlated random variates derived from the normaldistribution , 2003, IEEE Trans. Inf. Theory.

[10]  W. C. Jakes,et al.  Microwave Mobile Communications , 1974 .

[11]  H. W. Nylund Characteristics of small-area signal fading on mobile circuits in the 150 MHz band , 1968 .

[12]  Jan C. Olivier,et al.  A Comparative Study of Deterministic and Stochastic Sum-of-Sinusoids Models of Rayleigh-Fading Wireless Channels , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[13]  Fady Alajaji,et al.  A Model for Correlated Rician Fading Channels Based on a Finite Queue , 2008, IEEE Transactions on Vehicular Technology.

[14]  Matthias Ptzold Mobile Radio Channels , 2011 .

[15]  Xiurong Ma,et al.  Efficient and accurate simulator for Rayleigh and Rician fading , 2012 .

[16]  Gordon L. Stüber,et al.  Comparative analysis of statistical models for the simulation of Rayleigh faded cellular channels , 2005, IEEE Transactions on Communications.

[17]  Norman C. Beaulieu,et al.  Novel Sum-of-Sinusoids Simulation Models for Rayleigh and Rician Fading Channels , 2006, IEEE Transactions on Wireless Communications.

[18]  Antonio Petrolino,et al.  A Mobile-to-Mobile Fading Channel Simulator Based on an Orthogonal Expansion , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.