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Proceedings Paper

An adaptive PID neuron network controller for congestion control
Author(s): Li Yu; Zibo Shi; Yantai Shu
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Paper Abstract

We propose a congestion controller based on the Proportional-Integral-Differential Neuron Network (PIDNN). As existing controllers, our controller employs the queue size in bottleneck link router as a congestion indicator to trigger packet dropping. The target queue length and the feedback, actual queue length, act as the controller's two input signals. The packet dropping probability is computed by PIDNN controller with its simple embedded algorithm in term of the predefined state function and output function. Thus, the dropping probability decides to drop or to accept an incoming packet so that the queue length is kept at (or near) the target level. This controller's performance is examined under various network configurations, and compared to proposed congestion algorithms, including PI and RED. Our simulation results show that, with comparable simple implementation, this scheme has short response time, better robustness, and more adaptability, especially under highly dynamic network and heavy traffic load.

Paper Details

Date Published: 10 September 2007
PDF: 10 pages
Proc. SPIE 6773, Next-Generation Communication and Sensor Networks 2007, 67730J (10 September 2007); doi: 10.1117/12.733055
Show Author Affiliations
Li Yu, Tianjin Univ. (China)
Zibo Shi, Tianjin Univ. (China)
Yantai Shu, Tianjin Univ. (China)


Published in SPIE Proceedings Vol. 6773:
Next-Generation Communication and Sensor Networks 2007
Sergey I. Balandin, Editor(s)

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