An Antenna Diversity-Aided Channel Network for Wireless Channel Estimation

To estimate channel response is a remarkably challenging task for the wireless communication systems due to the extremely fast time-varying characteristic of channel, such as vehicle-to-vehicle (V2V) channel. In this paper, an antenna diversity-aided neural network is proposed for wireless channel estimation. A demodulation reference signals-aided maximal ratio combining estimator is presented to extend the demodulation reference signals into tentative channel response. A super-resolution residual network is utilized to track the variations of V2V channel by extracting the time-frequency correlation of data symbols and a denoise network is used to removing the noise by extracting the noise distribution information of data symbols. Numerical simulation results show the effectiveness of the proposed an antenna diversity-aided channel network.