Near-Field ISAC in 6G: Addressing Phase Nonlinearity via Lifted Super-Resolution

TL;DR

This paper addresses the challenge of phase nonlinearity in near-field ISAC with ELAA in 6G, proposing a lifted super-resolution method that transforms the problem into a linear high-dimensional space for improved target detection and channel estimation.

Contribution

It introduces a novel lifted super-resolution framework that linearizes phase nonlinearity in near-field ISAC, enabling accurate joint target detection and channel estimation.

Findings

Effective linearization of phase nonlinearity in near-field scenarios

High-precision joint target detection and channel estimation

Framework applicable to practical 6G ISAC systems

Abstract

Integrated sensing and communications (ISAC) is a promising component of 6G networks, fusing communication and radar technologies to facilitate new services. Additionally, the use of extremely large-scale antenna arrays (ELAA) at the ISAC common receiver not only facilitates terahertz-rate communication links but also significantly enhances the accuracy of target detection in radar applications. In practical scenarios, communication scatterers and radar targets often reside in close proximity to the ISAC receiver. This, combined with the use of ELAA, fundamentally alters the electromagnetic characteristics of wireless and radar channels, shifting from far-field planar-wave propagation to near-field spherical wave propagation. Under the far-field planar-wave model, the phase of the array response vector varies linearly with the antenna index. In contrast, in the near-field spherical wave model, this phase relationship becomes nonlinear. This shift presents a fundamental challenge: the widely-used Fourier analysis can no longer be directly applied for target detection and communication channel estimation at the ISAC common receiver. In this work, we propose a feasible solution to address this fundamental issue. Specifically, we demonstrate that there exists a high-dimensional space in which the phase nonlinearity can be expressed as linear. Leveraging this insight, we develop a lifted super-resolution framework that simultaneously performs communication channel estimation and extracts target parameters with high precision.