  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.
