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 beam-forming, non-orthogonal multiple access (NOMA), etc. However, with the application of massive MIMO in 5G, 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.

Automated summary

This paper introduces a CSI feedback system framework CF-FCFNN based on deep learning, which can recover the original CSI more accurately from compressed CSI, solving the feedback overhead and challenges brought by massive MIMO in 5G.