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.

翻译：在频分双工（FDD）系统中，下行链路大规模多输入多输出（MIMO）预编码算法依赖于用户端准确的信道状态信息（CSI）反馈。本文分析了CSI反馈开销与用户所获系统性能（即可达速率）之间的权衡关系。所提系统的最终目标是根据信道实现确定波束赋形信息（即预编码）。我们采用基于深度学习的方法设计面向预编码的端到端反馈架构，该架构包含学习型导频、用户端压缩器以及基站处理模块。我们提出一种损失函数，在最小化反馈开销的同时最大化总可达速率之和。仿真结果表明，我们的方法优于先前的面向预编码方法，并且相较于将CSI压缩模块与预编码处理分离的传统方法，能够提供更高效的解决方案。