一种融合EMD分解和LSTM网络的频谱占用度预测模型

Spectrum Occupancy Prediction Model Based on EMD Decomposition and LSTM Networks

频谱占用度是衡量频谱利用率、反应频谱分配是否合理的重要依据,但是非稳态的频谱占用度序列为有效的预测带来了巨大的挑战。文中提出了融合EMD与LSTM的计算模型(EMD-LSTM),该模型首先对原始占用度序列进行经验模态分解(EMD),令其生成含有不同时间尺度的本征模函数(IMF),然后用Pearson相关系数选择出相关度高的IMF,并将其与频谱占用度序列进行融合,最后利用长短时记忆网络(LSTM)对融合序列进行占用度预测。仿真实验结果及分析表明,相比于普通的LSTM网络,新的模型在预测频谱占用度变化上有了较大的性能改善。

Spectrum occupancy is an important basis to measure the spectrum utilization rate and reflect whether the spectrum allocation is reasonable.However,the unsteady spectrum occupancy sequence presents great challenges for effective prediction.In this paper,a new computing model(EMD-LSTM)combining EMD and LSTM is proposed.Firstly,the empirical mode decomposition(EMD)of the original occupancy sequence is used to generate the Intrinsic Mode Function(IMF)with different time scales,and then the highly correlated IMF is selected by Pearson correlation coefficient.Then,IMF and spectrum occupancy sequence are fused,and the occupancy sequence is predicted by using the long and short time memory network(LSTM).Simulation experiments and analysis show that,compared with the ordinary LSTM network,the new model has a great improvement in predicting the change of spectrum occupancy.