A novel self-tuning feedback controller for active queue management supporting TCP flows

Naixue Xiong, Athanasios V. Vasilakos, Laurence T. Yang, Cheng Xiang Wang, Rajgopal Kannan, Chin Chen Chang, Yi Pan

Research output: Contribution to journalArticlepeer-review

94 Citations (Scopus)


Wireless access points act as bridges between wireless and wired networks. Since the actually available bandwidth in wireless networks is much smaller than that in wired networks, there is a bandwidth disparity in channel capacity which makes the access point a significant network congestion point. The recently proposed active queue management (AQM) is an effective method used in wired network and wired-wireless network routers for congestion control, and to achieve a tradeoff between channel utilization and delay. The de facto standard, the random early detection (RED) AQM scheme, and most of its variants use average queue length as a congestion indicator to trigger packet dropping. In this paper, we propose a Novel autonomous Proportional and Differential RED algorithm, called NPD-RED, as an extension of RED. NPD-RED is based on a self-tuning feedback proportional and differential controller, which not only considers the instantaneous queue length at the current time point, but also takes into consideration the ratio of the current differential error signal to the buffer size. Furthermore, we give theoretical analysis of the system stability and give guidelines for the selection of feedback gains for the TCP/RED system to stabilize the instantaneous queue length at a desirable level. Extensive simulations have been conducted with ns2. The simulation results have demonstrated that the proposed NPD-RED algorithm outperforms the existing AQM schemes in terms of average queue length, average throughput, and stability. © 2009.

Original languageEnglish
Pages (from-to)2249-2263
Number of pages15
JournalInformation Sciences
Issue number11
Publication statusPublished - 1 Jun 2010


  • Active queue management
  • Autonomous
  • Congestion control
  • Random early detection (RED)
  • Self-tuning feedback control
  • Stability


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