PolarDenseNet: A Deep Learning Model for CSI Feedback in MIMO Systems
This addresses a bottleneck in improving performance for FDD MIMO systems, particularly in multi-user scenarios, though it appears incremental as it builds on existing auto-encoder approaches.
The paper tackles the problem of high feedback overhead for channel state information (CSI) in FDD MIMO systems by proposing an AI-based auto-encoder model, which outperforms the state-of-the-art 5G NR codebook in reducing overhead while minimizing recovery loss.
In multiple-input multiple-output (MIMO) systems, the high-resolution channel information (CSI) is required at the base station (BS) to ensure optimal performance, especially in the case of multi-user MIMO (MU-MIMO) systems. In the absence of channel reciprocity in frequency division duplex (FDD) systems, the user needs to send the CSI to the BS. Often the large overhead associated with this CSI feedback in FDD systems becomes the bottleneck in improving the system performance. In this paper, we propose an AI-based CSI feedback based on an auto-encoder architecture that encodes the CSI at UE into a low-dimensional latent space and decodes it back at the BS by effectively reducing the feedback overhead while minimizing the loss during recovery. Our simulation results show that the AI-based proposed architecture outperforms the state-of-the-art high-resolution linear combination codebook using the DFT basis adopted in the 5G New Radio (NR) system.