  Downlink massive multiple-input multiple-output (MIMO) precoding algorithms
in frequency division duplexing (FDD) systems rely on accurate channel state
information (CSI) feedback from users. In this paper, we analyze the tradeoff
between the CSI feedback overhead and the performance achieved by the users in
systems in terms of achievable rate. The final goal of the proposed system is
to determine the beamforming information (i.e., precoding) from channel
realizations. We employ a deep learning-based approach to design the end-to-end
precoding-oriented feedback architecture, that includes learned pilots, users'
compressors, and base station processing. We propose a loss function that
maximizes the sum of achievable rates with minimal feedback overhead.
Simulation results show that our approach outperforms previous
precoding-oriented methods, and provides more efficient solutions with respect
to conventional methods that separate the CSI compression blocks from the
precoding processing.
