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Understanding the nature of traffic has been a key concern of the researchers particularly over the last two decades and it has been noticed through extensive high quality studies that traffic found in different kinds of IP/wireless IP networks is human operators . Despite the recent findings of real time human behavior in measured traffic from data networks, much of the current understanding of IP traffic modeling is still based on simplistic probability distributed traffic. Unlike most existing studies that are primarily based on simplistic probabilistic model and traditional scheduling algorithms, this research presents an analytical performance model for real time human behavior queue systems with intelligent task management traffic input scheduled by a novel and promising scheduling mechanism for 4G-LTE system. Our proposed model is substantiated on human behavior queuing system that considers real time of traffic exhibiting homogeneous tasks characteristics. We analyze the model on the basis of newly proposed scheduling scheme for 4G-LTE system. We present closed form expressions of expected response times for real time traffic classes. We develop a discrete event simulator to understand the behavior of real time of arriving tasks traffic under this newly proposed scheduling mechanism for 4GLTE system . The results indicate that our proposed scheduling algorithm provides preferential treatment to real-time applications such as voice and video but not to that extent that data applications are starving for bandwidth and outperforms all other scheduling schemes that are available in the market.

Performance Simulation and Analysis for LTE System Using Human Behavior Queue Model