Exploiting Bi-Directional Channel Reciprocity in Deep Learning for Low Rate Massive MIMO CSI Feedback

Channel state information (CSI) feedback is important for multiple-input multiple-output (MIMO) wireless systems to achieve their capacity gain in frequency division duplex (FDD) mode. For massive MIMO systems, CSI feedback may consume too much bandwidth and degrade spectrum efficiency. This work proposes a learning-based CSI feedback framework based on limited feedback and bi-directional reciprocal channel characteristics. The massive MIMO base station exploits the available uplink CSI to help recovering the unknown downlink CSI from low rate user feedback. We propose two deep learning architectures, DualNet-MAG and DualNet-ABS, to significantly reduce the CSI feedback payload based on the multipath reciprocity. DualNet-MAG and DualNet-ABS can exploit the bi-directional correlation of the magnitude and the absolute value of real/imaginary parts of the CSI coefficients, respectively. Experiment results demonstrate that our architectures bring an obvious improvement compared with the downlink-based architecture.