Accurate estimation of DL CSI is required to achieve high spectrum and energy efficiency in massive MIMO systems. Previous works have developed learning-based CSI feedback framework within FDD systems for efficient CSI encoding and recovery with demonstrated benefits. However, downlink pilots for CSI estimation by receiving terminals may occupy excessively large number of resource elements for massive number of antennas and compromise spectrum efficiency. To overcome this problem, we propose a new learning-based feedback architecture for efficient encoding of partial CSI feedback of interleaved non-overlapped antenna subarrays by exploiting CSI temporal correlation. For ease of encoding, we further design an IFFT approach to decouple partial CSI of antenna subarrays and to preserve partial CSI sparsity. Our results show superior performance in indoor/outdoor scenarios by the proposed model for CSI recovery at significantly reduced computation power and storage needs.
翻译:为了在大型MIMO系统中实现高频谱和能源效率,需要对DL CSI进行准确估计。以前的工作是在DDFD系统中开发了基于学习的CSI反馈框架,以便高效率的CSI编码和回收并展示效益。然而,通过接收终端对CSI进行下行估算的试点项目可能占用大量资源要素,用于大量天线和折射频谱效率。为了解决这一问题,我们提议一个新的基于学习的反馈结构,以便通过利用CSI时间相关性,高效率地将CSI对断开的未覆盖天线次阵列的部分CSI反馈进行编码。为了便于编码,我们进一步设计了FFFFT方法,将天线次阵列部分CSI脱钩,并保存部分CSI宽度。我们的结果显示,根据CSI拟议模型,在大幅降低计算能力和储存需求的情况下恢复的室内/室外情景表现优异。