F$^4$-CKM: Learning Channel Knowledge Map with Radio Frequency Radiance Field Rendering

TL;DR

F$^4$-CKM introduces a novel framework for constructing channel knowledge maps in 6G communications by leveraging radiance field rendering and environment-aware techniques, significantly improving prediction accuracy and efficiency.

Contribution

The paper presents a new CKM construction framework using radiance field rendering and a Wireless Radiator Representation network, enabling environment-aware, location-Free, and fast channel prediction.

Findings

Outperforms existing methods in prediction accuracy

Achieves higher efficiency in CKM construction

Demonstrates robustness across diverse wireless environments

Abstract

In 6G mobile communications, acquiring accurate and timely channel state information (CSI) becomes increasingly challenging due to the growing antenna array size and bandwidth. To alleviate the CSI feedback burden, the channel knowledge map (CKM) has emerged as a promising approach by leveraging environment-aware techniques to predict CSI based solely on user locations. However, how to effectively construct a CKM remains an open issue. In this paper, we propose F$^4$-CKM, a novel CKM construction framework characterized by four distinctive features: radiance Field rendering, spatial-Frequency-awareness, location-Free usage, and Fast learning. Central to our design is the adaptation of radiance field rendering techniques from computer vision to the radio frequency (RF) domain, enabled by a novel Wireless Radiator Representation (WiRARE) network that captures the spatial-frequency characteristics of wireless channels. Additionally, a novel shaping filter module and an angular sampling strategy are introduced to facilitate CKM construction. Extensive experiments demonstrate that F$^4$-CKM significantly outperforms existing baselines in terms of wireless channel prediction accuracy and efficiency.