BeamCKM: A Framework of Channel Knowledge Map Construction for Multi-Antenna Systems

TL;DR

This paper introduces BeamCKM, a novel framework for constructing channel knowledge maps in multi-antenna systems, utilizing advanced deep learning architectures to improve accuracy over existing methods.

Contribution

The paper proposes BeamCKM and CKMTransUNet, novel deep learning architectures tailored for multi-antenna channel mapping, addressing limitations of traditional single-antenna approaches.

Findings

Outperforms state-of-the-art interpolation and deep learning methods.

Maintains high accuracy even with environmental contour inaccuracies.

Provides publicly available code for reproducibility.

Abstract

The channel knowledge map (CKM) enables efficient construction of high-fidelity mapping between spatial environments and channel parameters via electromagnetic information analysis. Nevertheless, existing studies are largely confined to single-antenna systems, failing to offer dedicated guidance for multi-antenna communication scenarios. To address the inherent conflict between traditional real-value pathloss map and multi-degree-of-freedom (DoF) coherent beamforming in B5G/6G systems, this paper proposes a novel concept of BeamCKM and CKMTransUNet architecture. The CKMTransUNet approach combines a UNet backbone for multi-scale feature extraction with a vision transformer (ViT) module to capture global dependencies among encoded linear vectors, utilizing a composite loss function to characterize the beam propagation characteristics. Furthermore, based on the CKMTransUNet backbone, this paper presents a methodology named M3ChanNet. It leverages the multi-modal learning technique and cross-attention mechanisms to extract intrinsic side information from environmental profiles and real-time multi-beam observations, thereby further improving the map construction accuracy. Simulation results demonstrate that the proposed method consistently outperforms state-of-the-art (SOTA) interpolation methods and deep learning (DL) approaches, delivering superior performance even when environmental contours are inaccurate. For reproducibility, the code is publicly accessible at https://github.com/github-whh/BeamCKM.