Environment-Aware Near-Field Channel Estimation Leveraging CKM and ISAC

2026-04-05

AI Summary

This work addresses the challenges in near-field channel estimation for extremely large-scale antenna arrays, where the absence of environmental priors and the coexistence of static and dynamic multipath components complicate accurate modeling. To tackle this, the paper integrates Channel Knowledge Maps (CKM) with Integrated Sensing and Communication (ISAC) techniques, introducing a Virtual Object Map (VOM) to characterize static multipath reflections and leveraging monostatic radar sensing to capture dynamic target information. A sensing-aided channel training protocol is devised to enable joint estimation of both static and dynamic multipath components. Experimental results demonstrate that the proposed approach significantly outperforms conventional methods in terms of channel estimation accuracy and achievable rate. The core innovation lies in the VOM representation and the synergistic near-field channel modeling framework that unifies sensing and communication.

Abstract

This paper proposes an environment-aware near-field channel estimation framework for integrated sensing and communication (ISAC) systems equipped with extremely large-scale antenna arrays (ELAAs). The proposed framework jointly exploits channel knowledge maps (CKMs) and ISAC to obtain a priori information on static and dynamic environmental features for facilitating channel estimation. In particular, we propose a novel CKM representation, termed the virtual object map (VOM), which stores the locations of virtual environment objects (EOs) to characterize the dominant multipath components (MPCs) induced by static physical EOs. In addition, we design a sensing-assisted channel training protocol, in which the ISAC-enabled base station (BS) transmits downlink pilots while simultaneously collecting monostatic echoes for sensing dynamic targets in the environment, and the user equipment (UE) feeds back a quantized version of its received pilot observation. Based on the VOM prior and the sensed dynamic information, the BS jointly estimates the coefficients of the static and dynamic MPCs to recover the near-field channel. Numerical results demonstrate that the proposed joint VOM- and sensing-aided channel estimation scheme significantly outperforms conventional schemes without VOM-based priors and/or dynamic sensing in terms of both channel estimation accuracy and achievable rate.

Problem

• near-field channel estimation
• integrated sensing and communication
• channel knowledge maps
• extremely large-scale antenna arrays
• multipath components

Innovation

• Channel Knowledge Map (CKM)
• Integrated Sensing and Communication (ISAC)
• Virtual Object Map (VOM)
• Near-Field Channel Estimation
• Extremely Large-Scale Antenna Array (ELAA)