Massive MIMO wireless FDD systems are often confronted by the challenge to efficiently obtain downlink channel state information (CSI). Previous works have demonstrated the potential in CSI encoding and recovery by take advantage of uplink/downlink reciprocity between their CSI magnitudes. However, such a framework separately encodes CSI phase and magnitude. To improve CSI encoding, we propose a learning-based framework based on limited CSI feedback and magnitude-aided information. Moving beyond previous works, our proposed framework with a modified loss function enables end-to-end learning to jointly optimize the CSI magnitude and phase recovery performance. Simulations show that the framework outperforms alternate approaches for phase recovery over overall CSI recovery in indoor and outdoor scenarios.

翻译：海事组织的大规模无线捍卫民主力量系统往往面临有效获取下链路频道状态信息的挑战。以前的工作已经表明,通过利用CSI数量之间的上链/下链对等,CSI编码和复苏的潜力。然而,这样一个框架单独编码CSI阶段和规模。为了改进CSI编码,我们提议了一个基于有限CSI反馈和规模辅助信息的学习框架。除了以前的工程外,我们拟议的框架,经过修改的损失功能使终端到终端学习能够共同优化CSI规模和阶段恢复绩效。模拟表明,框架比室内和室外总体 CSI恢复的替代方法更有利于分阶段恢复。