优化的EPON前摄Polling机制

Predictive Polling Mechanism on EPON

利用由多个关键报告生成的插值多项式预测方程来预测未来几个周期的业务量,通过1个消息(grant)对多个周期的授权,节省了带宽和减少了数据包的时延,建立了知识支撑系统(KSS)系统,通过偏导(DC)寻优法来优化关键周期和预测周期的个数,通过神经网络来优化非线形预测方程的调节因子.仿真结果很好地验证了它的效果.

The mechanism forecasts the traffic for many cycles through the prediction function of interpolating polynomial produced by the reports of many key cycles, and grants many cycles at a time. Not only can it save bandwidth, but also reduce the data packet delay. A knowledge support system （KSS） is set up. The key cycle number and predictive cycle number is optimized by the differential coefficient （DC） optimizing method, the adaptive factors of the non - lineal prediction function are optimized by the neural network. Simulation verifies its merits very well.