By Topic

An adaptive AQM algorithm based on neuron reinforcement learning

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Chuan Zhou ; Sch. of Autom., Nanjing Univ. of Sci. & Technol., Nanjing, China ; Dongjie Di ; Chen, Qingwei ; Jian Guo

In recent years, it has become an active research direction to develop adaptive and robust active queue management (AQM) scheme for congestion control of complex time-varying network. A novel adaptive AQM scheme based on neuron reinforcement learning (NRL) is presented in this paper. This scheme uses queue length and link rate as congestion notification to determine an appropriate drop/mark probability, and the parameters of neuron can be adjusted online according to the time-varying network environment so that the stability of queue dynamics and robustness for fluctuation of TCP loads are guaranteed. This scheme is easy to implement with simple structure, and it is independent of the model of plant to be controlled. Simulation results show that this proposed algorithm is especially suitable for solving the complex network congestion control problem, and also has better stability and robustness.

Published in:

Control and Automation, 2009. ICCA 2009. IEEE International Conference on

Date of Conference:

9-11 Dec. 2009