基于LSTM神经网络的卫星频谱多门限感知算法

A Satellite Spectrum Multi-threshold Sensing AlgorithmBased on LSTM Neural Network

针对在卫星认知通信场景下传统频谱感知算法感知性能低、受通信时延影响大的问题,提出了一种基于长短期记忆(LSTM)神经网络的卫星频谱多门限感知算法。首先构建卫星认知通信模型,其次将仿真数据送入长短期记忆(LSTM)神经网络进行预测感知,采用动量随机梯度下降(SGDM)算法对网络进行更新,然后提出多门限算法对网络输出进行优化,最后与其他神经网络算法作性能对比。该算法无需构建特征值,实验结果表明:在卫星信道条件下,当面对低接收信噪比及低网络迭代次数时,该算法频谱感知性能要优于其他神经网络算法。

A satellite spectrum multi-threshold sensing algorithm based on long short-term memory(LSTM)neural network is proposed in allusion to the problems of low sensing performance and high influence of communication delay in satellite cognitive communication.Firstly,the satellite cognitive communication model is constructed,then the simulation data is sent to the LSTM neural network for prediction and sensing,and the network is updated by the stochastic gradient descent with momentum(SGDM)algorithm.Then the multi-threshold algorithm is proposed to optimize the output of LSTM neural network,and finally,the performance is compared with other neural network algorithms.The results show that under the condition of satellite channel,the spectrum sensing performance of the proposed algorithm is better than that of other neural network algorithms in low signal-to-noise ratio(SNR)and low neural network iterations.