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Research of QoS Optimization Based on Neural Network Prediction for Ethernet Passive Optical Network

Author: LuoJinHua
Tutor: LiuDeMing
School: Huazhong University of Science and Technology
Course: Optical Engineering
Keywords: Ethernet passive optical network Dynamic bandwidth allocation Neural Networks Nonlinear Business forecasting
CLC: TN929.1
Type: Master's thesis
Year: 2011
Downloads: 8
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Abstract


Ethernet passive optical network (EPON) is currently accepted mainstream of optical access network technology , dynamic bandwidth allocation (DBA) mechanism is a key technology used in the the scheduling coordination uplink service bandwidth control upstream traffic quality of service ( QoS ) a key instrument for this research is of great significance for enhancing system performance . OLT and ONU communication is the interaction of five control frame through the Multi - Point Control Protocol (MPCP) , accurate predictions ONU waiting for authorization within the frame time arrival data packets , can effectively reduce packet delay and improve system uplink efficiency . The common scenario for linear prediction suited to network traffic at low load will continue to produce high burst phenomenon of self-similar long - relevant characteristics . In this thesis, the introduction of nonlinear prediction of network business DBA mechanism to form a report in response to the combined business forecast scheduling mechanisms and algorithms in order to optimize the the EPON system uplink in multi-service QoS performance . This dissertation first summarizes Ethernet passive optical network system architecture and communication protocols , and detailed analysis from the scheduling mechanism and authorized length algorithm the existing dynamic bandwidth allocation scheme , followed by in-depth analysis of network business since long characteristics and physical meaning , interpretation of the principle of the neural network , using MATLAB tools to simulate and verify the accuracy of the neural network forecasting network traffic , and finally proposed dynamic bandwidth allocation algorithm based on neural network ( NPBA ) , and simulation experiments based simulation model based on OPNET tool . Simulation results show that the neural network model can be more accurate nonlinear prediction the the NPBA algorithm based on compared to general DBA algorithm can effectively reduce the end-to-end delay , while taking into account the system throughput , inhibition of delay jitter , and optimize the quality of service .

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