Composite {\alpha}-{\mu} Based DSRC Channel Model Using Large Data Set of RSSI Measurements
Abstract
Channel modeling is essential for design and performance evaluation of numerous protocols in vehicular networks. In this work, we study and provide results for largescale and small-scale modeling of communication channel in dense vehicular networks. We first propose an approach to remove the effect of fading on deterministic part of the large-scale model and verify its accuracy using a single transmitter-receiver scenario. Two-ray model is then utilized for path-loss characterization and its parameters are derived from the empirical data based on a newly proposed method. Afterward, we use {\alpha}-{\mu} distribution to model the fading behavior of vehicular networks for the first time, and validate its precision by Kolmogorov-Smirnov (K-S) goodness-of-fit test. To this end, the significantly better performance of utilizing {\alpha}-{\mu} distribution over the most adopted fading distribution in the vehicular channels literature, i.e. Nakagami-m, in terms of passing K-S test has been investigated and statistically verified in this paper. A large received signal strength indicator (RSSI) dataset from a measurement campaign is used to evaluate our claims. Moreover, the whole model is implemented in a reliable discrete event network simulator which is widely used in the academic and industrial research for network analysis, i.e. network simulator-3 (ns-3), to show the outcome of the proposed model in the presence of upper layer network protocols.
Cite
@article{arxiv.1808.00509,
title = {Composite {\alpha}-{\mu} Based DSRC Channel Model Using Large Data Set of RSSI Measurements},
author = {Hossein Nourkhiz Mahjoub and Amin Tahmasbi-Sarvestani and S M Osman Gani and Yaser P. Fallah},
journal= {arXiv preprint arXiv:1808.00509},
year = {2018}
}