  Massive MIMO basestations, operating with frequency-division duplexing (FDD),
require the users to feedback their channel state information (CSI) in order to
design the precoding matrices. Given the powerful capabilities of deep neural
networks in learning quantization codebooks, utilizing these networks in
compressing the channels and reducing the massive MIMO CSI feedback overhead
has recently gained increased interest. Learning one model, however, for the
full cell or sector may not be optimal as the channel distribution could change
significantly from one \textit{zone} (an area or region) to another. In this
letter, we introduce the concept of \textit{zone-specific} CSI feedback. By
partitioning the site space into multiple channel zones, the underlying channel
distribution can be efficiently leveraged to reduce the CSI feedback. This
concept leverages the implicit or explicit user position information to select
the right zone-specific model and its parameters. To facilitate the evaluation
of associated overhead, we introduce two novel metrics named \textit{model
parameters transmission rate} (MPTR) and \textit{model parameters update rate}
(MPUR). They jointly provide important insights and guidance for the system
design and deployment. Simulation results show that significant gains could be
achieved by the proposed framework. For example, using the large-scale Boston
downtown scenario of DeepMIMO, the proposed zone-specific CSI feedback approach
can on average achieve around 6dB NMSE gain compared to the other solutions,
while keeping the same model complexity.
