AI enabled wireless communications with real channel measurements: Channel feedback

Artificial intelligence (AI) has shown great potential in wireless communications. AI-empowered communication algorithms have beaten many traditional algorithms through simulations. However, the existing works just use the simulated datasets to train and test the algorithms, which can not represent the power of AI in practical communication systems. Therefore, Peng Cheng Laboratory holds an AI competition, National Artificial Intelligence Competition (NAIC): AI+wireless communications, in which one of the topics is AI-empowered channel feedback system design using practical measurements. In this paper, we give a baseline neural network design, QuanCsiNet, for this competition, and the details of the channel measurements. QuanCsiNet shows excellent performance on channel feedback and the complexity of the neural networks is also given.