基于调制卷积神经网络的空地数据链信道估计

Aimed at the inaccuracy of channel estimation of orthogonal frequency division multiplexing (OFDM) system in the complex air-ground data link environment, this paper proposes a channel estimation algorithm based on the modulated convolutional neural network (MCNN) and bidirectional long short-term memory (BiLSTM) network. First, least square (LS) algorithm is used to extract the initial channel state information (CSI), then MCNN network is used to extract the depth characteristics of the initial CSI while compressing the network model, and finally BiLSTM network is used to predict the final CSI and realize channel estimation. In the aspect of experimental verification, the air-ground channel model constructed is used to generate the channel coefficient dataset, so as to realize the training and testing of neural network model. The simulation results show that compared with the traditional methods and the existing deep learning method, the proposed channel estimation method has a lower estimation error, and the performance of the bit error ratio (BER) of the system under the condition of high SNR is improved by nearly an order of magnitude. Due to the introduction of the modulation filter technology, the number of network model parameters decreases remarkably with the increase of the number of neural network layers.