基于长短期记忆网络的无人机认知无线电频谱预测

UAV cognitive radio spectrum prediction based on long short-term memory network

为解决无线电频谱利用率低的问题,提出利用长短期记忆网络(LSTM)来预测一段时间内的无线电频谱信息,使得装有认知无线电的无人机能够机会性的接入频谱空洞,以提高无线电频谱的利用率。首先分别建立整合移动平均自回归(ARIMA),时延神经网络(TDNN)以及LSTM的预测模型,其次构建3种预测模型相应的预测算法,最后利用提出的LSTM预测模型与同样具有时序型预测模型的ARIMA和TDNN在预测误差上的比较,证明该LSTM网络在预测具有时序型的无人机频谱信息中具有更好的预测性能。

In order to solve the problem of low radio frequency spectrum utilization,a long short-term memory network(LSTM)is proposed to predict the radio frequency spectrum information over a period of time.Enables drones equipped with cognitive radios to access spectrum holes opportunistically to improve the utilization of radio spectrum.First,establish a prediction model auto-regressive integrated moving average(ARIMA),time delay neural network(TDNN)and LSTM.Secondly,construct the corresponding prediction algorithms for the three prediction models.Finally,use the proposed LSTM prediction model to compare the prediction errors with ARIMA and TDNN,which also have time series prediction models.Prove that the LSTM network has better predictive performance in predicting time-series UAV spectrum information.