Keypoint Detection Empowered Near-Field User Localization and Channel Reconstruction

TL;DR

This paper introduces a fast, deep learning-based approach for near-field user localization and channel reconstruction in XL MIMO systems, replacing computationally intensive search methods with keypoint detection networks.

Contribution

It proposes CKNet and FlexibleCKNet models for rapid, accurate localization and channel reconstruction, reducing computational complexity compared to traditional compressed sensing methods.

Findings

Significantly reduces computational complexity

Maintains high localization and reconstruction accuracy

Effective in both Cartesian and Polar domains

Abstract

In the near-field region of an extremely large-scale multiple-input multiple-output (XL MIMO) system, channel reconstruction is typically addressed through sparse parameter estimation based on compressed sensing (CS) algorithms after converting the received pilot signals into the transformed domain. However, the exhaustive search on the codebook in CS algorithms consumes significant computational resources and running time, particularly when a large number of antennas are equipped at the base station (BS). To overcome this challenge, we propose a novel scheme to replace the high-cost exhaustive search procedure. We visualize the sparse channel matrix in the transformed domain as a channel image and design the channel keypoint detection network (CKNet) to locate the user and scatterers in high speed. Subsequently, we use a small-scale newtonized orthogonal matching pursuit (NOMP) based refiner to further enhance the precision. Our method is applicable to both the Cartesian domain and the Polar domain. Additionally, to deal with scenarios with a flexible number of propagation paths, we further design FlexibleCKNet to predict both locations and confidence scores. Our experimental results validate that the CKNet and FlexibleCKNet-empowered channel reconstruction scheme can significantly reduce the computational complexity while maintaining high accuracy in both user and scatterer localization and channel reconstruction tasks.