Method for selecting codebooks based on deep learning under large scale MIMO

The invention relates to a method for selecting codebooks based on deep learning under large scale MIMO(Multiple-Input Multiple-Output) and belongs to the technical field of wireless communication. The method comprises following steps: acquiring pilot frequency information of a test zone to establish a pilot frequency training sequence and further obtaining a pilot frequency training sample; performing neural network iteration learning to the pilot frequency sample to obtain a final network weight value; selecting optical code words from a complete codebook according to the signal channel output by the neural network after learning. performing signal channel information matching to an unknown zone and the test zone to obtain a wireless signal channel thereof, and further obtaining code words corresponding to the wireless signal channel. By means of the method, wireless signal model and codebook query can be effectively, accurately and quickly established to avoid signal channel estimation of unknown zones and greatly reduce the complexity of unknown zone signal channel codebook selection.