Zone-Specific CSI Feedback for Massive MIMO: A Situation-Aware Deep Learning Approach

Abstract

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. In this letter, we introduce the concept of zone-specific CSI feedback. By partitioning the site space into multiple channel zones (areas or regions), the underlying channel distribution can be efficiently leveraged using deep learning models 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 model parameters transmission rate (MPTR) and model parameters update rate (MPUR). They jointly provide important insights and guidance for the system design and deployment. Simulation results show that noticeable 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.