基于多任务学习的MIMO-OFDM信噪比估计与调制识别

The research on blind estimation of signal-to-noise ratio and modulation recognition of subcarriers in multi input multi output orthogonal frequency division multiplexing (MIMO-OFDM) signals in non cooperative communication is only aimed at a single task. For this reason, an algorithm combining deep neural network with multi task learning model is proposed to complete blind estimation of signal-to-noise ratio and modulation recognition of subcarriers at the same time. Firstly, the transmitted signal is recovered using the joint approximate diagonalization (JADE) algorithm of the eigenvalue matrix, and the co orthogonal components of the recovered signal are extracted as shallow features; Then, a multi-task learning model based on one-dimensional convolutional neural network is built. Through joint training of signal-to-noise ratio estimation and subcarrier modulation recognition, the advantages are complementary. Simulation results show that the proposed algorithm can achieve better performance than the single task learning model; When the signal-to-noise ratio is -10 dB, the mean square error of signal-to-noise ratio estimation is reduced by 66.21%, and the accuracy of subcarrier modulation recognition is improved by 4.75%.