A study on OFDM channel estimation based on inverse convolutional neural network

This topic takes Orthogonal Frequency Division Multiplexing (OFDM) as the research object to study the new idea of channel estimation using Inverse Convolutional Neural Network. This paper focuses on the in-depth study and improvement of the channel estimation method based on the guide frequency. Existing channel estimation algorithms based on the guide frequency mainly use the interpolation method to make full use of the channel characteristics in which the guide frequency is located, so as to obtain the complete channel state information. Compared to the interpolation method, a more accurate method is used to model the relationship between the position of the guide frequency and the entire channel. Using the channel simulation system of the University of Vienna, the collected signals are preprocessed and the deconvolutional neural network is learned offline using an offline method to reduce the channel fading. The results show that the algorithm proposed in this paper has better channel distortion and transmit signal detection compared to the MMSE algorithm.