  Massive MIMO systems can achieve high spectrum and energy efficiency in
downlink (DL) based on accurate estimate of channel state information (CSI).
Existing works have developed learning-based DL CSI estimation that lowers
uplink feedback overhead. One often overlooked problem is the limited number of
DL pilots available for CSI estimation. One proposed solution leverages
temporal CSI coherence by utilizing past CSI estimates and only sending
CSI-reference symbols (CSI-RS) for partial arrays to preserve CSI recovery
performance. Exploiting CSI correlations, FDD channel reciprocity is helpful to
base stations with direct access to uplink CSI. In this work, we propose a new
learning-based feedback architecture and a reconfigurable CSI-RS placement
scheme to reduce DL CSI training overhead and to improve encoding efficiency of
CSI feedback. Our results demonstrate superior performance in both indoor and
outdoor scenarios by the proposed framework for CSI recovery at substantial
reduction of computation power and storage requirements at UEs.
