Abstract
This paper examines explicit rate congestion control for data networks. A neural network (NN) adaptive controller is developed to control traffic where sources regulate their transmission rates in response to the feedback information from network switches. Particularly, the queue length dynamics at a given switch is modeled as an unknown nonlinear discrete time system with cell propagation delay and bounded disturbances. To overcome the effects of delay an iterative transformation is introduced for the future queue length prediction. Then based on the causal form of the dynamics in buffer an adaptive NN controller is designed to regulate the queue length to track a desired value. The convergence of our scheme is derived mathematically. Finally, the performance of the proposed congestion control scheme is also evaluated in the presence of propagation delays and time-vary available bandwidth for robustness considerations.
Supported in part by the NSFC (No. 60174010) and the Key Research Project of Ministry of Education (No. 204014). This work was also supported in part by Yanshan University (No. YDJJ2003010).
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Yang, B., Guan, X. (2004). A Neural Network Adaptive Controller for Explicit Congestion Control with Time Delay. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_11
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DOI: https://doi.org/10.1007/978-3-540-28648-6_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
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