Channel estimation for OFDM system based on deep learning - Yun ZHANG; Jing ZHOU; Jingwei HUANG; Shujuan YU; Liya HUANG (Tongxin xuebao)
An efficient channel estimation model based on deep learning was proposed for the problems of inter-carrier interference and inter-symbol interference in 5G system signal reception.The estimated channels were obtained through a preliminary estimation at the pilots.And they were treated as low resolution images containing noise, which were input into the channel estimation model.By learning the mapping relationship between the low resolution images and the high resolution images, the noise in input channels was removed, and the high-resolution channel images were restored to obtain the entire channel state information eventually.The simulation results show that the model not only continues the advantages of traditional attention mechanisms in suppressing redundant information, reduces computational overhead, but also achieves good accuracy and robustness, and has good estimation performance for various channels.