Fully Connected Feedforward Neural Networks Based CSI Feedback Algorithm

In modern wireless communication systems, the accurate acquisition of channel state information(CSI) is critical to the performance of beamforming, non-orthogonal multiple access(NOMA),etc. However, with the application of massive MIMO in 5 G, the number of antennas increases by hundreds or even thousands times, which leads to excessive feedback overhead and poses a huge challenge to the conventional channel state information feedback scheme. In this paper, by using deep learning technology, we develop a system framework for CSI feedback based on fully connected feedforward neural networks(FCFNN), named CF-FCFNN. Through learning the training set composed of CSI, CF-FCFNN is able to recover the original CSI from the compressed CSI more accurately compared with the existing method based on deep learning without increasing the algorithm complexity.