基于深度学习的无人机数据链信噪比估计算法

UAV data link communication is subject to natural and artificial interferences. The signal-to-noise ratio (SNR) is an effective evaluation indicator of channel state and communication quality. In order to address insufficient SNR estimation accuracy involved in traditional estimation algorithm, an estimation model which combines convolutional neural networks (CNN) and long short term memory (LSTM) network is proposed. By means of both simulation and actual measurement, a data set of UAV communication signals is constructed with multiple SNRs, modulation modes, fading channels and other information included. In the network training phase, the sample sequence is segmented, CNN-LSTM is used to extract the deep feature of each part, and the model parameters are saved through multiple trainings. In the test phase, the constructed test set is used to verify and test the algorithm, and the SNR estimation value is obtained. Experiments show that compared with traditional SNR estimation algorithm and single-network deep learning method, the proposed algorithm can help achieve the lowest mean square error for different levels of SNR, thus achieving the high-precision estimation of SNR.