Precoding-oriented Massive MIMO CSI Feedback Design
This work addresses the problem of efficient channel state information feedback for precoding in massive MIMO systems, offering incremental improvements for wireless communication networks.
The paper tackles the tradeoff between CSI feedback overhead and user performance in FDD massive MIMO systems by designing an end-to-end deep learning-based architecture for precoding-oriented feedback, which outperforms previous methods and conventional separated approaches in simulation results.
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.