DL-based CSI Feedback and Cooperative Recovery in Massive MIMO

Abstract

In this paper, we exploit the correlation between nearby user equipment (UE) and develop a deep learning-based channel state information (CSI) feedback and cooperative recovery framework, CoCsiNet, to reduce the feedback overhead. The CSI information can be divided into two parts: shared by nearby UE and owned by individual UE. The key idea of exploiting the correlation is to reduce the overhead used to repeatedly feedback shared information. Unlike in the general autoencoder framework, an extra decoder and a combination network are added at the base station to recover the shared information from the feedback CSI of two nearby UE and combine the shared and individual information, respectively, but no modification is performed at the UEs. For a UE with multiple antennas, we also introduce a baseline neural network architecture with long short-term memory modules to extract the correlation of nearby antennas. Given that the CSI phase is not sparse, we propose two magnitude-dependent phase feedback strategies that introduce statistical and instant CSI magnitude information to the phase feedback process, respectively. Simulation results on two different channel datasets show the effectiveness of the proposed CoCsiNet.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl