Deep Learning-Based CKM Construction with Image Super-Resolution

TL;DR

This paper introduces a deep learning approach using SRResNet for constructing comprehensive channel knowledge maps from sparse data, significantly improving accuracy and efficiency in wireless environment awareness.

Contribution

It presents a novel deep learning-based CKM construction method that does not require additional inputs and applies image super-resolution techniques to wireless channel mapping.

Findings

Outperforms interpolation and SRGAN methods in CKM accuracy.

Achieves low RMSE with only 1/16 measurements.

Extends to construct channel angle maps using CKMImageNet dataset.

Abstract

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.