RMTransformer: Accurate Radio Map Construction and Coverage Prediction
Yuxuan Li, Cheng Zhang, Wen Wang, Yongming Huang

TL;DR
This paper introduces RMTransformer, a hybrid transformer-convolution model that significantly improves radio map prediction accuracy, reducing RMSE by over 30% compared to existing methods, thereby enhancing wireless network modeling.
Contribution
The paper presents a novel hybrid transformer-convolution architecture for radio map prediction, combining multi-scale transformer encoding with convolution decoding for better accuracy.
Findings
Over 30% reduction in RMSE compared to SOTA methods
Enhanced prediction accuracy for radio maps
Effective feature extraction with transformer components
Abstract
Radio map, or pathloss map prediction, is a crucial method for wireless network modeling and management. By leveraging deep learning to construct pathloss patterns from geographical maps, an accurate digital replica of the transmission environment could be established with less computational overhead and lower prediction error compared to traditional model-driven techniques. While existing state-of-the-art (SOTA) methods predominantly rely on convolutional architectures, this paper introduces a hybrid transformer-convolution model, termed RMTransformer, to enhance the accuracy of radio map prediction. The proposed model features a multi-scale transformer-based encoder for efficient feature extraction and a convolution-based decoder for precise pixel-level image reconstruction. Simulation results demonstrate that the proposed scheme significantly improves prediction accuracy, and over a…
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Taxonomy
TopicsMillimeter-Wave Propagation and Modeling · Indoor and Outdoor Localization Technologies · Advanced Data Compression Techniques
