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.

翻译：信道知识地图（CKM）是实现环境感知无线通信与感知的一项前景广阔的技术。基于稀疏位置的信道知识观测构建完整的CKM，是CKM赋能的无线网络面临的基础性问题。然而，现有大多数关于CKM构建的研究仅考虑了CKM的特殊类型，即信道增益图（CGM），它仅记录每个位置的信道增益值。本文研究的是信道空间相关图（SCM）的构建，它表征多天线系统中位置特定的空间相关矩阵。与CGM构建不同，SCM构建因其极高的维度结构而面临重大挑战。为解决此问题，我们首先将高维SCM分解为低维的路径增益图（PGM）和路径角度图（PAM）。随后，我们提出了一种名为E-SRResNet的深度学习模型，用于从稀疏样本中构建高质量的SCM。该模型融合了多头注意力（MHA）机制和多尺度特征融合（MSFF），以精确建模信道参数的局部与全局空间关系及复杂的非线性映射。此外，我们对数据集进行预处理，为模型提供先验信息，包括视距（LoS）图、建筑物二值图及基站（BS）图，从而更准确地重建SCM。在CKMImageNet数据集上进行的仿真实验表明，所提出的E-SRResNet在性能上显著优于基线方法。更重要的是，在大多数区域，构建的SCM与真实值之间的余弦相似度超过0.8，验证了所提构建方法的有效性。