(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 6, No. 10, 2015 298 | Page www.ijacsa.thesai.org A Simulation Model for Nakagmi-m Fading Channel with m>1 Sandeep Sharma School of ICT Gautam Buddha University Greater Noida, India Rajesh Mishra School of ICT Gautam Buddha University Greater Noida, India Abstract—In this paper, we propose a model to simulate a wireless fading channel based on Nakagami-m distribution with m>1. The Nakagami-m fading channel is the most generalized distribution as it can generate one-sided Gaussian distribution, Rayleigh distribution and Rician distribution for m equals to 0.5, 1 and >1 respectively. In this work we have proposed a method to generate a wireless fading channel based on Nakagami-m distribution as this distribution fits to a wide class of fading channel conditions. Simulation results were obtained using Matlab R2013a and compared with the analytical results. Keywords—Nakagami Distribution; Fading Channel; Wireless Channel Modeling I. I NTRODUCTION In wireless communication fading plays a vital role in the channel estimation. Fading is the rapid fluctuation in the received signal strength of the wireless signal. Communication systems are subjected to fading caused by multipath propagation due to reflections by surrounding objects, refractions and scattering by buildings and other large structures. Thus, the received signal is a sum of various signals that arrive at the receiver via different propagation paths which may be direct line of sight (LOS) or non line of sight path (non-LOS). To model fading in wireless communication, several techniques have been used in literature. As the nature of the wireless channel is random, it has to be model statistically. Several statistical models have been used in the literature to describe the fading envelope of the received signal [6],[13]-[15]. The Rayleigh and Rician distributions are used to characterize the fading envelope of the wireless signals over small geographical areas or short term fades while the log- normal distribution is used when much wider geographical areas are involved. A more versatile statistical model, however, is Nakagami’s m-distribution [1], which can model a variety of fading environments including those modeled by the Rayleigh and one-sided Gaussian distributions. Also the log-normal and Rician distributions may be closely approximated by the Nakagami distribution in some ranges of mean signal values [l6]. The fit between Nakagami and Rician distributions is very accurate for low signal-to-noise ratio (SNR) values in comparison to large SNR values. Furthermore, the Nakagami distribution is more flexible and more accurately fit experimental data for many physical propagation channels then the log-normal and Rician distributions [l6],[17]. We may find various research papers where Nakagami distribution is used to simulate in applications like satellite communication, vehicular to vehicular communication, even it is applied in medical applications such as ECG and ultrasound signals. Although the Nakagami model fits experimental data around the mean or median, but it is reported in [18] that it does not fit very well in the tails of the distribution. In spite this, the Nakagami distribution is much popular and used by many of the researchers in their domain whether it may be wireless, medical, terrestrial signal analysis, and vehicular ad-hoc networks. Paper Organization: This paper is organized as follows. In section II, we discussed the theoretical background of wireless channel modeling and the factors affecting it. Section III explains the Nakagami-m distribution followed by the simulation method in section IV. Section V discusses the various results and their analysis. Finally, section VI. II. THEORITICAL BACKGROUND In this section, we explain theoretical background of a wireless channel and the factors affecting the channel response. A wireless channel is different from a wired channel as it contains multipath components from direct line of sight (LOS) component and various reflected and refracted components. The wireless channel is made by the constructive and destructive addition of different multipath components introduced by the channel. The same phase components are added and the out of phase components are subtracted and their algebraic sum is what we get at the antenna of the receiver. In general, the deterministic channel models are rarely available as the nature of the channel is random, and thus we need to characterize multipath channels statistically. If a single pulse is transmitted over a multipath channel, then the received signal will not be a single pulse but appear as a series of pulses, with each pulse in the series corresponds to the LOS component or an individual multipath component associated with a discrete scatterer or cluster of scatterers. The channel characteristics certainly depends upon the number of scatterer objects, number of multipath, size of the objects and the amount of absorption by the surrounding environment such as wall and roofs (thickness and material has a impact on the degree of absorption). Another characteristic of the multipath channel is its time-varying nature. This variation in time arises because either the transmitter or the receiver is moving, and this mobility of the transmitter and/ or receiver therefore change the location of reflectors in the transmission path, which give rise to multipath, will change over time.