用深度学习从稀疏数据重建无线信道地图，精度远超传统方法。
Deep Learning-Based CKM Construction with Image Super-Resolution
- 基于SRResNet网络，仅用稀疏测量数据实现信道知识图构建。
- 仅需1/16位置采样即达1.1 dB的路径损耗均方根误差。
- 可推广至角度图构建，适用于智能通信与感知系统设计。
信道知识图（CKM）是一种新型环境感知技术，能提升无线系统的通信与感知性能。其核心挑战是仅凭稀疏测量数据构建覆盖大量位置的完整CKM，该问题与图像超分辨率（SR）类似。本文提出一种基于深度学习的高效CKM构建方法，采用经典的图像超分辨率网络SRResNet。与多数现有研究不同，本方法仅需稀疏测量数据作为输入，无需额外信息。除常规路径损耗图外，借助新构建的数据集CKMImageNet，该方法还可用于构造信道角度图（CAM）。数值结果表明，该方法在CKM构建中优于最近邻、双三次插值及SRGAN等方法。仅需1/16位置采样，即可实现路径损耗的均方根误差（RMSE）为1.1 dB。
原文摘要 · Abstract (English)
Channel knowledge map (CKM) is a novel technique for achieving environment awareness, and thereby improving the communication and sensing performance for wireless systems. A fundamental problem associated with CKM is how to construct a complete CKM that provides channel knowledge for a large number of locations based solely on sparse data measurements. This problem bears similarities to the super-resolution (SR) problem in image processing. In this letter, we propose an effective deep learning-based CKM construction method that leverages the image SR network known as SRResNet. Unlike most existing studies, our approach does not require any additional input beyond the sparsely measured data. In addition to the conventional path loss map construction, our approach can also be applied to construct channel angle maps (CAMs), thanks to the use of a new dataset called CKMImageNet. The numerical results demonstrate that our method outperforms interpolation-based methods such as nearest neighbour and bicubic interpolation, as well as the SRGAN method in CKM construction. Furthermore, only 1/16 of the locations need to be measured in order to achieve a root mean square error (RMSE) of 1.1 dB in path loss.
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