PolarDenseNet: A Deep Learning Model for CSI Feedback in MIMO Systems

TL;DR

This paper introduces PolarDenseNet, a deep learning auto-encoder model that significantly reduces CSI feedback overhead in MIMO systems, outperforming traditional methods in 5G NR simulations.

Contribution

The paper presents a novel AI-based auto-encoder architecture for CSI feedback that improves over existing linear codebook methods in MIMO systems.

Findings

Outperforms state-of-the-art high-resolution codebooks

Reduces feedback overhead effectively

Maintains low loss during CSI recovery

Abstract

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.