# An I2I Inpainting Approach for Efficient Channel Knowledge Map Construction arXiv: https://arxiv.org/abs/2406.09822 ยท HTML: https://mlsanity.com/paper/2406.09822/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 20 of its 43 references that are in the map. - First posted on arXiv 14 Jun 2024. ## Builds on (most cited first, 20 of 20) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017) - [XCiT: Cross-Covariance Image Transformers](https://mlsanity.com/paper/2106.09681/) (2021) - [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019) - [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021) - [Aggregated Contextual Transformations for High-Resolution Image Inpainting](https://mlsanity.com/paper/2104.01431/) (2021) - [On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds](https://mlsanity.com/paper/2302.14536/) (2023) - [High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network](https://mlsanity.com/paper/2105.09188/) (2021) - [A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G](https://mlsanity.com/paper/2309.07460/) (2023) - [Deep Completion Autoencoders for Radio Map Estimation](https://mlsanity.com/paper/2005.05964/) (2020) - [Radio Map Based 3D Path Planning for Cellular-Connected UAV](https://mlsanity.com/paper/1912.00021/) (2019) - [Joint Device Selection and Power Control for Wireless Federated Learning](https://mlsanity.com/paper/2205.09306/) (2022) - [Spatial Signal Strength Prediction using 3D Maps and Deep Learning](https://mlsanity.com/paper/2011.03597/) (2020) - [Adaptive Bayesian Radio Tomography](https://mlsanity.com/paper/1804.02084/) (2018) - [Environment-Aware and Training-Free Beam Alignment for mmWave Massive MIMO via Channel Knowledge Map](https://mlsanity.com/paper/2102.09871/) (2021) - [Environment-Aware Beam Selection for IRS-Aided Communication with Channel Knowledge Map](https://mlsanity.com/paper/2111.11289/) (2021) - [Optimal User-Cell Association for Massive MIMO Wireless Networks](https://mlsanity.com/paper/1407.6731/) (2014) - [Beam Squint-Aware Integrated Sensing and Communications for Hybrid Massive MIMO LEO Satellite Systems](https://mlsanity.com/paper/2203.00235/) (2022) - [Cellular-Base-Station Assisted Device-to-Device Communications in TV White Space](https://mlsanity.com/paper/1506.01394/) (2015) ## Built on by (most cited first, 13 of 13) - [GDM4MMIMO: Generative Diffusion Models for Massive MIMO Communications](https://mlsanity.com/paper/2412.18281/) (2024) - [CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors](https://mlsanity.com/paper/2504.17323/) (2025) - [Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information](https://mlsanity.com/paper/2505.06900/) (2025) - [Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection](https://mlsanity.com/paper/2604.05788/) (2026) - [Deep Learning-Based CKM Construction with Image Super-Resolution](https://mlsanity.com/paper/2411.08887/) (2024) - [ClearSight: Human Vision-Inspired Solutions for Event-Based Motion Deblurring](https://mlsanity.com/paper/2501.15808/) (2025) - [CF-CGN: Channel Fingerprints Extrapolation for Multi-band Massive MIMO Transmission based on Cycle-Consistent Generative Networks](https://mlsanity.com/paper/2412.20885/) (2024) - [Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach](https://mlsanity.com/paper/2505.07893/) (2025) - [EnvCDiff: Joint Refinement of Environmental Information and Channel Fingerprints via Conditional Generative Diffusion Model](https://mlsanity.com/paper/2505.07894/) (2025) - [Channel Knowledge Map Construction via Physics-Inspired Diffusion Model Without Prior Observations](https://mlsanity.com/paper/2512.02757/) (2025) - [Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensing](https://mlsanity.com/paper/2607.00887/) (2026) - [CSI-tuples-based 3D Channel Fingerprints Construction Assisted by MultiModal Learning](https://mlsanity.com/paper/2603.25288/) (2026) - [Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework](https://mlsanity.com/paper/2604.27574/) (2026) ## Abstract (arXiv) Channel knowledge map (CKM) has received widespread attention as an emerging enabling technology for environment-aware wireless communications. It involves the construction of databases containing location-specific channel knowledge, which are then leveraged to facilitate channel state information (CSI) acquisition and transceiver design. In this context, a fundamental challenge lies in efficiently constructing the CKM based on a given wireless propagation environment. Most existing methods are based on stochastic modeling and sequence prediction, which do not fully exploit the inherent physical characteristics of the propagation environment, resulting in low accuracy and high computational complexity. To address these limitations, we propose a Laplacian pyramid (LP)-based CKM construction scheme to predict the channel knowledge at arbitrary locations in a targeted area. Specifically, we first view the channel knowledge as a 2-D image and transform the CKM construction problem into an image-to-image (I2I) inpainting task, which predicts the channel knowledge at a specific location by recovering the corresponding pixel value in the image matrix. Then, inspired by the reversible and closed-form structure of the LP, we show its natural suitability for our task in designing a fast I2I mapping network. For different frequency components of LP decomposition, we design tailored networks accordingly. Besides, to encode the global structural information of the propagation environment, we introduce self-attention and cross-covariance attention mechanisms in different layers, respectively. Finally, experimental results show that the proposed scheme outperforms the benchmark, achieving higher reconstruction accuracy while with lower computational complexity. Moreover, the proposed approach has a strong generalization ability and can be implemented in different wireless communication scenarios.