An artificial intelligence approach to price design for improving AQM performance

Hao Wang, Jiezhi Chen, Chenda Liao, Zuohua Tian

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)


Active queue management (AQM) mechanism is a powerful method, which aims to assist the TCP congestion control and to improve the trade-off between queuing delay and link utilization. Traditional price-based AQM algorithms suffer from sluggish response, poor robustness, and lack adequate adaptability against dynamic traffics. To improve AQM performance, this paper introduces artificial intelligence methods to design a sophisticated AQM algorithm. In particular, a fuzzy neuron price is developed for congestion detection. Hebbian learning rule and fuzzy logic theory are employed to configure the control parameters automatically for better adaptability and robustness. Simulation results demonstrate that our proposed scheme is stable, responsive and performs robustly against time-varying network dynamics. It is superior to other peer AQM algorithms in various performance indicators, such as stability and jitter of queue length as well as packet loss.

Original languageEnglish
Title of host publication2011 IEEE Global Telecommunications Conference, GLOBECOM 2011
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Print)9781424492688
Publication statusPublished - 2011
Externally publishedYes
Event54th Annual IEEE Global Telecommunications Conference: "Energizing Global Communications", GLOBECOM 2011 - Houston, TX, United States of America
Duration: 5 Dec 20119 Dec 2011


Conference54th Annual IEEE Global Telecommunications Conference: "Energizing Global Communications", GLOBECOM 2011
CountryUnited States of America
CityHouston, TX

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