You May Use the Same Channel Knowledge Map for Environment-Aware NLoS Sensing and Communication

TL;DR

This paper introduces a novel environment-aware NLoS sensing method in 6G wireless networks using a channel knowledge map (CKM) that serves both sensing and communication, enhancing localization in complex environments.

Contribution

The paper proposes reusing the same CKM for wireless communication and sensing, enabling NLoS sensing in complex environments with improved accuracy.

Findings

Significant performance improvements over classic geometry-based sensing methods.

Validation of the approach through extensive simulations.

CRLB analysis confirms the effectiveness of the proposed method.

Abstract

As one of the key usage scenarios for the sixth generation (6G) wireless networks, integrated sensing and communication (ISAC) provides an efficient framework to achieve simultaneous wireless sensing and communication. However, traditional wireless sensing techniques mainly rely on the line-of-sight (LoS) assumptions, i.e., the sensing targets are directly visible to both the sensing transmitter and receiver. This hinders ISAC systems to be applied in complex environments such as the urban low-altitude airspace, which usually suffers from signal blockage and non-line-of-sight (NLoS) multi-path propagation. To address this challenge, in this paper, we propose a novel approach to enable environment-aware NLoS ISAC by leveraging the new technique called channel knowledge map (CKM), which was originally proposed for environment-aware wireless communications. One major novelty of our proposed method is that the same CKM built for wireless communication can be directly used to enable NLoS wireless sensing, thus enjoying the benefits of ``killing two birds with one stone''. To this end, the sensing targets are treated as virtual user equipment (UE), and the wireless communication channel priors are transformed into the sensing channel priors, allowing one single CKM to serve dual purposes. We illustrate our proposed framework by a specific CKM called \emph{channel angle-delay map} (CADM). Specifically, the proposed framework utilizes CADM to derive angle-delay priors of the sensing channel by exploiting the relationship between communication and sensing angle-delay distributions, enabling sensing target localization in the challenging NLoS environment. Extensive simulation results demonstrate significant performance improvements over classic geometry-based sensing methods, which is further validated by Cram\'er-Rao Lower Bound (CRLB) analysis.