Abstract
In this fast growing world, there is a great demand for multimedia applications in WiMAX networks. To fulfill this demand, the network should be capable of handling QoS of traffic. Hence, we are predicting the QoS of traffic in the network. This is a prediction based problem. To solve this problem, we have applied two algorithms, namely, Response Surface Methodology (RSM) and Radial Basis Function Neural Network (RBFNN) on two different applications, namely, CBR based traffic and file transfer applications. From the experiment, we have observed that RBFNN performs better than RSM.
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Acknowledgment
The authors would like to thank PES Institute of Technology, Bangalore, for providing the infrastructure and resources in completing this work successfully.
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Sangeetha, J., Harish Kumar, G., Jindal, A. (2017). Quality of Service of Traffic Prediction Mechanism in WiMAX Network Using RBFNN and RSM. In: Deiva Sundari, P., Dash, S., Das, S., Panigrahi, B. (eds) Proceedings of 2nd International Conference on Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 467. Springer, Singapore. https://doi.org/10.1007/978-981-10-1645-5_5
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DOI: https://doi.org/10.1007/978-981-10-1645-5_5
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