Deep Learning-Based CSI Feedback for XL-MIMO Systems in the Near-Field Domain

TL;DR

This paper introduces ExtendNLNet, a deep learning model designed to efficiently compress and recover CSI in near-field XL-MIMO systems, addressing the challenges posed by spherical wave characteristics and large CSI matrices.

Contribution

The paper proposes a novel DL-based ExtendNLNet with Non-Local blocks for improved CSI compression and feedback in near-field XL-MIMO systems, a scenario with highly complex channel characteristics.

Findings

ExtendNLNet outperforms existing DL methods in CSI recovery quality.

The model effectively captures large CSI feature areas using Non-Local blocks.

Simulation results demonstrate significant improvement in feedback efficiency.

Abstract

In this paper, we consider an extremely large-scale massive multiple-input-multiple-output (XL-MIMO) system. As the scale of antenna arrays increases, the range of near-field communications also expands. In this case, the signals no longer exhibit planar wave characteristics but spherical wave characteristics in the near-field channel, which makes the channel state information (CSI) highly complex. Additionally, the increase of the antenna arrays scale also makes the size of the CSI matrix significantly increase. Therefore, CSI feedback in the near-field channel becomes highly challenging. To solve this issue, we propose a deep-learning (DL)-based ExtendNLNet that can compress the CSI, and further reduce the overhead of CSI feedback. In addition, we have introduced the Non-Local block to obtain a larger area of CSI features. Simulation results show that the proposed ExtendNLNet can significantly improve the CSI recovery quality compared to other DL-based methods.