Deep Learning for Joint Channel Estimation and Feedback in Massive MIMO Systems

Understand

The great potentials of massive Multiple-Input Multiple-Output (MIMO) in Frequency Division Duplex (FDD) mode can be fully exploited when the downlink Channel State Information (CSI) is available at base stations.

• However, the accurate CSI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.
• In this paper, we propose a deep learning based joint channel estimation and feedback framework, which comprehensively realizes the estimation, compression, and reconstruction of downlink channels in FDD massive MIMO systems.
• Two networks are constructed to perform estimation and feedback explicitly and implicitly.