基于改进单神经元梯度学习的无线网络主动队列管理

doi:10.12066/j.issn.1007-2861.2172

Journal of Shanghai University（Natural Science Edition） ›› 2021, Vol. 27 ›› Issue (3): 553-562.doi: 10.12066/j.issn.1007-2861.2172

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Improved single neuron gradient learning-based active queue management for wireless networks

QI Aichun¹(), XU Lei²

1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
2. Department of Intelligent Operation and Maintenance Product Development, Nanjing ZTE NetView Software Co., Ltd., Nanjing 211153, China

Received:2018-09-26 Online:2021-06-30 Published:2021-06-27
Contact: QI Aichun E-mail:qac77@163.com

Abstract

Abstract:

This study introduces a scheme that takes the packet arrival link rate as the input of the controller, where the traditional network congestion control ignores the continuous state of network congestion. Then, an active queue management algorithm that improves single neuron gradient learning (ISNGL) is obtained. The algorithm uses gradient learning to adjust dynamically the network parameters and to improve the convergence rate and stability. The study also proposes a new activation function with displacement parameters and an improved method using momentum adjustment for weights. Finally, NS2 network simulation software is used to simulate a wireless network topology model. Results show that the ISNGL algorithm provides good congestion control capability in wireless networks.

Key words: single neuron, gradient learning, active queue management, NS2 network simulation, wireless networks

CLC Number:

TP393

QI Aichun, XU Lei. Improved single neuron gradient learning-based active queue management for wireless networks[J]. Journal of Shanghai University（Natural Science Edition）, 2021, 27(3): 553-562.

Figures/Tables 7

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References 11

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Improved single neuron gradient learning-based active queue management for wireless networks

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