Modeling of Multimedia Internet Transmission Rate Control Factors Using Neural Networks
| Autor: | Chong Kil-To, Yoo Sung-Goo |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Engineering
Artificial neural network Multimedia business.industry ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Real-time computing Round-trip delay time Perceptron computer.software_genre Transmission (telecommunications) Bandwidth (computing) The Internet business Protocol (object-oriented programming) computer Data transmission |
| Zdroj: | Journal of Control, Automation and Systems Engineering. 11:385-391 |
| ISSN: | 1225-9845 |
| DOI: | 10.5302/j.icros.2005.11.4.385 |
| Popis: | As the Internet real-time multimedia applications increases, the bandwidth available to TCP connections is oppressed by the UDP traffic, result in the performance of overall system is extremely deteriorated. Therefore, developing a new transmission protocol is necessary. The TCP-friendly algorithm is an example satisfying this necessity. The TCP-Friendly Rate Control (TFRC) is an UDP-based protocol that controls the transmission rate that is based on the available round trip time (RTT) and the packet loss rate (PLR). In the data transmission processing, transmission rate is determined based on the conditions of the previous transmission period. If the one-step ahead predicted values of the control factors are available, the performance will be improved significantly. This paper proposes a prediction model of transmission rate control factors that will be used in the transmission rate control, which improves the performance of the networks. The model developed through this research is predicting one-step ahead variables of RTT and PLR. A multiplayer perceptron neural network is used as the prediction model and Levenberg-Marquardt algorithm is used for the training. The values of RTT and PLR were collected using TFRC protocol in the real system. The obtained prediction model is validated using new data set and the results show that the obtained model predicts the factors accurately. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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