  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.
