Estimated Measurement-Based Markov Models: Towards Flexible and Accurate Modeling of Wireless Channels

Han, Seon-Yeong; Abu-Ghazaleh, Nael

doi:10.1109/wimob.2009.63

Cited by 2 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the network community, Dhoutaut et al [8] propose a propagation model based on Markov chains elements and real world experiments which is able to generate packet losses in a very realistic way. Later on, Han and Abu-Ghazaleh proposed another method based on Finite State Markov model [9]. These models are half-way between very detailed models using ray-tracing with computationally intensive algorithms and models using theoretical analysis where physical phenomena are only handled in an aggregate manner.…”

Section: Introductionmentioning

confidence: 99%

A Semi-Deterministic Channel Model for VANETs Simulations

Ledy

Boeglen

Poussard

et al. 2012

International Journal of Vehicular Technology

View full text Add to dashboard Cite

Today's advanced simulators facilitate thorough studies on Vehicular Ad hoc NETworks (VANETs). However the choice of the physical layer model in such simulators is a crucial issue that impacts the results. A solution to this challenge might be found with a hybrid model. In this paper, we propose a semi-deterministic channel propagation model for VANETs called UM-CRT. It is based on CRT (Communication Ray Tracer) and SCME—UM (Spatial Channel Model Extended—Urban Micro) which are, respectively, a deterministic channel simulator and a statistical channel model. It uses a process which adjusts the statistical model using relevant parameters obtained from the deterministic simulator. To evaluate realistic VANET transmissions, we have integrated our hybrid model in fully compliant 802.11 p and 802.11 n physical layers. This framework is then used with the NS-2 network simulator. Our simulation results show that UM-CRT is adapted for VANETs simulations in urban areas as it gives a good approximation of realistic channel propagation mechanisms while improving significantly simulation time.

show abstract

Section: Introductionmentioning

confidence: 99%