DeReNet: WIRELESS FADING CHANNEL ESTIMATION MODEL BASED ON DEEP NEURAL NETWORK

In order to improve the communication fading channel estimation performance of OFDM (Orthogonal Frequency Division Multiplexing) system, a channel estimation model DeReNet based on deep neural network is proposed. DeReNet suppressed the gradient explosion and disappearance problems in network training by cascading deep dense networks and deep residual networks. In order to verify the effectiveness of DeReNet, DeReNet model was compared with LS, FC-DNN and SimNet models. The simulation results show that in the Rice fading environment, the channel estimation performance of DeReNet is better than that of three compared models, and the DeReNet model can effectively improve the estimation accuracy of channel fading.