Location-Agnostic Channel Knowledge Map Construction for Dynamic Scenes

TL;DR

This paper introduces LAD-CKM, a location-agnostic framework for dynamic channel knowledge map construction that predicts CSI accurately in highly mobile outdoor scenes using RF radiance field rendering and adaptive deformation.

Contribution

The paper proposes LAD-CKM, a novel dynamic CKM framework that leverages RF radiance field rendering and an adaptive deformation module for improved CSI prediction in mobile scenarios.

Findings

LAD-CKM outperforms existing methods in data rate.

The framework effectively captures dynamic scene variations.

Simulation results validate the approach's robustness.

Abstract

To alleviate the pilot and CSI-feedback burden in 6G, channel knowledge map (CKM) has emerged as a promising approach that predicts CSI solely from user locations. Nevertheless, accurate location information is rarely available in current systems. Moreover, the uncertainty inherent to highly dynamic scenes further degrades the performance of existing schemes that typically assume quasi-static scenarios. In this paper, we propose a novel framework named location-agnostic dynamic CKM (LAD-CKM). Specifically, LAD-CKM is constructed through dynamic radio frequency (RF) radiance field rendering, which takes instantaneous uplink CSI and partial downlink CSI as inputs. To enable effective rendering, a dedicated radiator representation network (RARE-Net) is designed to capture the spatial-spectral correlations within the inputs. Furthermore, an adaptive deformation module is devised to deform the uplink CSI-based queries of RARE-Net according to instantaneous channel dynamics, thereby enhancing CSI prediction accuracy under mobility. In addition, a novel synthetic channel dataset is created in outdoor dynamic scenes via ray-tracing. Simulation results demonstrate that LAD-CKM yields significant performance gains compared with existing baselines in terms of effective data rate.