Mr. Juraj Pavlovic Profile

Juraj Pavlovic

Student at Slovak Technical Univ in Bratislava

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Proceedings Article | 7 October 2011 Paper

Markov chain error generator for wireless channel

J. Pavlovič, J. Polec, J. Poctavek, K. Kotuliaková

Proceedings Volume 8008, 800814 (2011) https://doi.org/10.1117/12.905207

KEYWORDS: Error analysis, Data modeling, Modeling, Statistical analysis, Wireless communications, Mathematical modeling, Networks, Computer simulations, Complex systems, Network architectures

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Significant advance in the networks design led to the need of optimizing the future communication protocols with the aim to evaluate the network performance. The most common techniques include simulation, analytical models and analysis of empirical data. Accurate error process modelling is necessary for understanding the network behaviour and is essential in designing of error control protocols or real time applications. Discrete Markov models, mainly the Gilbert model, are commonly used for modelling the network error characteristics. This approach is based on the analysis of a communication on link. In this paper we present error model for packet loss and subsequent error description. The proposed model simulates the error performance of wireless communication link. The model is designed as two independent Markov chains, where the first one is used for packet generation and the second one generates correctly and incorrectly transmitted bits for received packets from the first chain. The statistical analyses of real communication on the wireless link are used for determination of model’s parameters. The obtained results generated by proposed model are compared with the real data collection. It is also designed and programmed generator for received traffic. First part of this generator is a generator for packet loss. Second part is a generator for bit error rate. Using the obtained parameters and the implementation of the generator, we collected generated traffic. The obtained results generated by proposed model are compared with the real data collection.

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