Statistical LOS/NLOS Channel Model for Simulations of Next Generation 3GPP Networks

Abstract

Understanding of line-of-sight and non-line-ofsight (LOS/NLOS) visibility conditions is important for radio wave propagation modeling when selecting empirical path loss model. For Monte Carlo statistical simulations a random generator of visibility states can be designed according to a given spatial probability density. In the current paper, a statistical LOS state model is derived based on ITU-R and 3GPP 3D LOS probability models suitable for 5G channel simulations. The generic exponential LOS probability model is improved by dividing the area into LOS and NLOS zones depending on the building density. The accuracy of LOS probability model has been tested against visibility predictions obtained from the digital building data over Manhattan city area.

Statistical Approximations of LOS/NLOS Probability in Urban Environment

“…where d is the distance in meters and d i , i = {1, 2, 3, 4} are empirical data fit coefficients. Similar LOS/NLOS probability models are suggested in other proposals [5], [6] and are discussed in more detail in [4]. Characteristic feature of such models is the existence of direct LOS visibility region around base station up to distance d 1 for urban environment.…”

“…To better represent real propagation environments which usually contain nonuniform building blocks of varying height, a combined dual environment model which approximates LOS probability over distance using two probability functions was proposed in [3], [4]. The composite LOS probability is characterized by two separate regions with different LOS probabilities p…”

“…Dual environment LOS/NLOS boundary approximation has already been introduced in [3] for Manhattan grid with regular street geometry at the same time maintaining spatial consistency for visibility state distribution. Digital building models of Manhattan city have been used in [4] to model realworld urban scenarios with trigonometric dual environment boundaries.…”

MS Location Estimation Based on the Artificial Bee Colony Algorithm

“…As the procedure of obtaining the target positions in the NLOS condition can be well approximated to solve a nonlinear system, several algorithms [ 25 , 26 , 27 , 28 ] have been developed to find the solutions with high accuracy. The scheme in [ 25 ] classified the area covered by the BS signals into LOS and NLOS zones by employing support vector classification and trigonometric approximation. In [ 26 ], the model of target locations was expressed as a probability distribution function.…”