混合复用无源光网络中带有灰色预测的高效动态资源分配策略

针对混合复用无源光网络的用户接入距离增大，使得往返时延增加，造成网络中闲置时间增多，进而引起带宽浪费的问题，该文提出一种带有灰色预测的高效动态混合资源分配策略。通过对闲置时间内到达的数据进行灰色预测，动态确立光网络单元的带宽分配策略，以减小网络时延；利用波长结束时间的差异性，周期性地实时调整各波长的数据传输顺序，实现波长的高效利用和负载均衡。仿真结果表明，该动态资源分配策略有效地消除闲置时间的影响，且波长得到高效地利用，提高了带宽利用率的同时减少网络时延。

An Efficient Dynamic Resource Allocation Strategy with Grey Prediction in Hybrid Multiplex Passive Optical Network

With the access distance of users increasing in hybrid Time and Wavelength Division Multiplexing Passive Optical Networks (TWDM-PONs), the round trip time becomes very long, and idle time is increased in the network. It leads to the problem of bandwidth waste. In order to solve this issue, an efficient and hybrid dynamic resource allocation strategy with Grey Prediction (GP) is proposed. In order to decrease the network delay performance and avoid the waste of resource, the Grey Prediction model is utilized to predict the arrival data during the idle time and dynamically allocate bandwidth to optical network units. Utilizing the finishing time difference of data transmission on wavelengths, the higher transmission efficiency and load balance of wavelengths can be achieved by constantly adjusting the mode of wavelengths transmission periodically. The simulation results show that the proposed hybrid strategy can effectively eliminate the idle time to avoid waste of bandwidth and reduce network delay while making wavelengths efficiently used to improve resource utilization rate.