基于调制卷积神经网络的空地数据链信道估计

针对复杂环境下空地数据链正交频分复用(OFDM)系统信道估计精度不足的问题，提出了一种基于调制卷积神经网络(MCNN)和双向长短时记忆网络(BiLSTM)结合的信道估计算法。利用最小二乘算法(LS)提取初始信道状态信息(CSI)；利用MCNN网络提取初始CSI的深度特征，并对网络模型进行压缩；利用BiLSTM网络对最终CSI进行预测，实现信道估计。利用构建的空地信道模型生成信道系数数据集，实现神经网络模型的训练与测试。仿真结果表明:与传统算法和现有深度学习方法相比，所提出的信道估计算法具有更小的估计误差，高信噪比条件下的系统误码率(BER)性能提升接近一个数量级；由于引入了调制滤波器技术，随着神经网络层数增加，网络模型参数量大幅减少。 

Channel estimation of air-ground data link based on modulated convolutional neural network

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.