2024/01/15 by Yu Zhang, Zhang, Yu, Ahmed Alkhateeb +1 · 1 citation
Engineering · #Advanced MIMO Systems Optimization #Antenna Design and Analysis #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Millimeter-Wave Propagation and Modeling #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2401.07451
openalex publication_date 2024/01/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 zone (an area or region) to another. In this letter, we introduce the concept of 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 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 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.