EVALUACIÓN DEL DESEMPEÑO DE SISTEMAS DE RADIO COGNITIVO CON DIFERENTES DISTRIBUCIONES DEL TIEMPO DE SERVICIO DE LOS USUARIOS SECUNDARIOS (EVALUATION OF THE PERFORMANCE OF COGNITIVE RADIO SYSTEMS WITH DIFFERENT DISTRIBUTIONS OF THE SERVICE TIME OF SECONDARY USERS)

Diego García Olivares, Genaro Hernández Valdez, Sandra Lirio Castellanos López, Felipe Alejandro Cruz Pérez, Grethell Georgina Pérez Sánchez

Resumen


Este trabajo estudia el desempeño de sistemas de radio cognitivo con diferentes distribuciones de probabilidad del tiempo de servicio de los usuarios secundarios. Para ello, se desarrolló un simulador de eventos discretos del sistema de radio cognitivo. En particular, se considera que el tiempo de servicio de los usuarios secundarios sigue una distribución de probabilidad log-normal y ésta es aproximada mediante distribuciones de probabilidad hiper-exponenciales de diferente orden. Para el cálculo de los parámetros de las distribuciones hiper-exponenciales se utiliza el algoritmo de Maximización de la Esperanza (EM). Los resultados obtenidos muestran que, mediante la distribución hiper-exponencial se pueden aproximar diferentes distribuciones de probabilidad como la log-normal sin pérdida significativa en la precisión de los resultados numéricos de las diferentes métricas de desempeño. Este resultado es relevante porque facilita el tratamiento y análisis matemático de sistemas de radio cognitivo.

In this paper, performance evaluation of cognitive radio networks (CRNs) with different probability density functions for the service time of secondary users is studied. To this end, a discrete event simulation program that captures the fundamental aspects of CRNs is developed. In particular, it is assumed that the secondary service time of many real life applications is well characterized by the log-normal distribution. In this work, the log-normal model is systematically approximated by n-th order hyper-exponential distributions. The parameters of the n-th order hyper-exponential distribution are computed by the well-known Expectation Maximization (EM) algorithm. Numerical results show that, the hyper-exponential distribution can be used for approximating the log-normal behaviour of the secondary service time without significative loss of precision on the obtained results for the different performance metrics. This result is relevant because the mathematical (queueing) analysis of CRN with log-normal service time is possible by means of approximating the log-normal behavior of the service time by the hyper-exponential model.


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