基于改进单神经元梯度学习的无线网络主动队列管理

考虑传统网络拥塞控制忽略了网络拥塞的持续状态, 引入将数据包到达链路速率作为控制器输入的方案, 得到一种改进单神经元梯度学习(improves single neuron gradient learning, ISNGL)的主动队列管理算法. ISNGL 算法采用梯度学习动态调整网络参数, 并在此基础上对收敛速度和稳定性加以改进, 提出带有位移参数的新激活函数和带有权值调整的动量项的改进方法, 最后通过 NS2 网络仿真软件在无线网络的拓扑模型上进行仿真分析, 结果表明 ISNGL 算法在无线网络环境下拥有良好的拥塞控制能力.

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

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.