计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 294-298.doi: 10.11896/JsJkx.190700097
赵晓东1, 苏公瑾2, 李克利2, 成杰2, 徐江峰1
ZHAO Xiao-dong1, SU Gong-Jin2, LI Ke-li2, CHENG Jie2 and XU Jiang-feng1
摘要: 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。
中图分类号:
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