CKM Beyond Channel Gain: Spatial Correlation Map Construction with Deep Learning

TL;DR

This paper introduces a deep learning approach, E-SRResNet, for constructing high-dimensional spatial correlation maps in wireless communication, leveraging priors and advanced neural mechanisms to improve accuracy from sparse data.

Contribution

The paper proposes a novel deep learning model that decomposes high-dimensional SCM into lower-dimensional maps and incorporates priors for more accurate reconstruction.

Findings

E-SRResNet outperforms baseline methods in simulations.

Cosine similarity exceeds 0.8 in most regions, indicating high accuracy.

Incorporating priors improves SCM reconstruction quality.

Abstract

Channel knowledge map (CKM) is a promising technique to achieve environment-aware wireless communication and sensing. Constructing the complete CKM based on channel knowledge observations at sparse locations is a fundamental problem for CKM-enabled wireless networks. However, most existing works on CKM construction only consider the special type of CKM, i.e., the channel gain map (CGM), which only records the channel gain value for each location. In this paper, we consider the channel spatial correlation map (SCM) construction, which signifies the location-specific spatial correlation matrix for multi-antenna systems. Unlike CGM construction, constructing SCM poses significant challenges due to its extremely high-dimensional structure. To address this issue, we first decompose the high-dimensional SCM into lower-dimensional path gain map (PGM) and path angle map (PAM). Then we propose a deep learning model termed E-SRResNet for constructing high-quality SCM from sparse samples, which incorporates multi-head attention (MHA) mechanisms and multi-scale feature fusion (MSFF) to accurately model both local and global spatial relationships of channel parameters and complex nonlinear mappings. Furthermore, we preprocess the dataset to provide priors including line-of-sight (LoS) map, binary building map and base station (BS) map for the model to reconstruct SCM more accurately. Simulations conducted on the CKMImageNet dataset demonstrate that the proposed E-SRResNet achieves significant performance improvements over baseline methods. Moreover, the cosine similarity between the constructed SCM and the ground truth exceeds 0.8 in most regions, validating the effectiveness of the proposed construction method.