Deep Learning-Based CKM Construction with Image Super-Resolution

Shiyu Wang; Xiaoli Xu; Yong Zeng

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Deep Learning-Based CKM Construction with Image Super-Resolution

Shiyu Wang, Xiaoli Xu, Yong Zeng

Signal Processing · Submitted 28 Oct 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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.

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What the abstract itself states, as probabilities: claims first 0.10 · states limitation 0.14 · human participants 0.03 · controlled comparison 0.85 · randomized assignment 0.01 · states quantitative result 0.99 · compares with prior results 0.97 · mentions code or data availability 0.04.

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arXiv listing abstract v1. Results and the main claim are underlined; show every role.

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Channel knowledge map (CKM) is a novel technique for achieving environment awareness, and thereby improving the communication and sensing performance for wireless systems.	background 1.00	0.05	0.03	asserted 1.00
s1	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.	background 0.54	0.06	0.03	asserted 1.00
s2	This problem bears similarities to the super-resolution (SR) problem in image processing.	background 0.98	0.11	0.02	asserted 0.98
s3	In this letter, we propose an effective deep learning-based CKM construction method that leverages the image SR network known as SRResNet.	contribution 0.54	0.91	0.11	asserted 1.00
s4	Unlike most existing studies, our approach does not require any additional input beyond the sparsely measured data.	contribution 0.92	0.86	0.07	asserted 1.00
s5	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.	contribution 0.94	0.86	0.05	asserted 0.94
s6	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.	result 1.00	0.94	0.59	asserted 1.00
s7	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.	result 1.00	0.95	0.25	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.88.

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The paper

Original 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.

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2 arXiv papers cite this in 4 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 2 sentences without a confident label

Cites it as background 1 paper

In parallel, the emerging channel knowledge map (CKM) literature has expanded the construction paradigm toward image inpainting, super-resolution, diffusion generation, flow matching, beam-aware mapping, and cross-access-point inference [ref] .

Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection · Introduction · 0.96

What it cites · 1 sentence without a confident label

Cites as background 1 paper

The authors of [ref] treat channel knowledge as a 2-D image, framing the CKM estimation as an image-to-image (I2I) inpainting task that predicts channel knowledge at a specific location.

An I2I Inpainting Approach for Efficient Channel Knowledge Map Construction · Introduction · 0.88

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