论文标题
可变天线端口下多率CSI反馈的可扩展深度学习框架
A Scalable Deep Learning Framework for Multi-rate CSI Feedback under Variable Antenna Ports
论文作者
论文摘要
发射机处的通道状态信息（CSI）对于大规模的MIMO下行链路系统至关重要，以实现高光谱和能源效率。现有的作品通过降低用户反馈开销并提高恢复精度，为ENB/GNB提供了深入的学习架构，用于CSI反馈和恢复。但是，现有的DL体系结构往往是不灵活的，并且不可降低，因为通常会根据预设的天线对给定的压缩比进行训练。在这项工作中，我们基于分裂方法（DCA）开发了一个灵活且可扩展的学习框架。这种新的DCA架构可以灵活地容纳不同数量的3GPP天线端口和反馈压缩的动态水平。重要的是，它还可以通过允许UES进行反馈分割的下行链路CSI来大大降低计算复杂性和内存大小。我们进一步提出了一个少于1000个参数的多速率连续卷积编码器。测试结果表明，室内和室外通道的性能卓越，良好的可伸缩性和较低的复杂性。
Channel state information (CSI) at transmitter is crucial for massive MIMO downlink systems to achieve high spectrum and energy efficiency. Existing works have provided deep learning architectures for CSI feedback and recovery at the eNB/gNB by reducing user feedback overhead and improving recovery accuracy. However, existing DL architectures tend to be inflexible and non-scalable as models are often trained according to a preset number of antennas for a given compression ratio. In this work, we develop a flexible and scalable learning framework based on a divide-and-conquer approach (DCA). This new DCA architecture can flexibly accommodate different numbers of 3GPP antenna ports and dynamic levels of feedback compression. Importantly, it also significantly reduces computational complexity and memory size by allowing UEs to feedback segmented downlink CSI. We further propose a multi-rate successive convolution encoder with fewer than 1000 parameters. Test results demonstrate superior performance, good scalability, and low complexity for both indoor and outdoor channels.
