RMTransformer: Accurate Radio Map Construction and Coverage Prediction

• URL: http://arxiv.org/abs/2501.05190v2
• Date: Sat, 11 Jan 2025 07:33:56 GMT
• Title: RMTransformer: Accurate Radio Map Construction and Coverage Prediction
• Authors: Yuxuan Li, Cheng Zhang, Wen Wang, Yongming Huang,
• Abstract summary: This paper introduces a hybrid transformer-convolution model, termed RMTransformer, to enhance the accuracy of radio map prediction.<n>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.
• Score: 34.903128282947115
• License: http://creativecommons.org/licenses/by/4.0/
• 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 30% reduction in root mean square error (RMSE) is achieved compared to typical SOTA approaches.

Related papers

• Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Framework [57.994965436344195]
  Beamforming is a key technology in millimeter-wave (mmWave) communications that improves signal transmission by optimizing directionality and intensity. multimodal sensing-aided beam prediction has gained significant attention, using various sensing data to predict user locations or network conditions. Despite its promising potential, the adoption of multimodal sensing-aided beam prediction is hindered by high computational complexity, high costs, and limited datasets.
  arXiv  Detail & Related papers  (2025-04-07T15:38:25Z)
• A Neural Network Architecture Based on Attention Gate Mechanism for 3D Magnetotelluric Forward Modeling [1.5862483908050367]
  We propose a novel neural network architecture named MTAGU-Net, which integrates an attention gating mechanism for 3D MT forward modeling. A dual-path attention gating module is designed based on forward response data images and embedded in the skip connections between the encoder and decoder. A synthetic model generation method utilizing 3D Gaussian random field (GRF) accurately replicates the electrical structures of real-world geological scenarios.
  arXiv  Detail & Related papers  (2025-03-14T13:48:25Z)
• Two-Stage Radio Map Construction with Real Environments and Sparse Measurements [11.432502140838867]
  A first-predict-then-correct (FPTC) method is proposed by leveraging generative adversarial networks (GANs) A primary radio map is first predicted by a radio map prediction GAN (RMP-GAN) taking environmental information as input. Then, the prediction result is corrected by a radio map correction GAN (RMC-GAN) with sparse measurements as guidelines. Experimental results validate that the proposed FPTC-GANs method achieves the best radio map construction performance.
  arXiv  Detail & Related papers  (2024-10-08T09:15:27Z)
• Radio Map Prediction from Aerial Images and Application to Coverage Optimization [46.870065000932016]
  We focus on predicting path loss radio maps using convolutional neural networks. We show that state-of-the-art models developed for existing radio map datasets can be effectively adapted to this task. We introduce a new model that slightly exceeds the performance of the present state-of-the-art with reduced complexity.
  arXiv  Detail & Related papers  (2024-10-07T09:19:20Z)
• Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN [63.90647197249949]
  In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. In this paper, we present a cooperative radio map estimation approach enabled by the generative adversarial network (GAN)
  arXiv  Detail & Related papers  (2024-02-05T05:01:28Z)
• Radio Map Estimation -- An Open Dataset with Directive Transmitter Antennas and Initial Experiments [49.61405888107356]
  We release a dataset of simulated path loss radio maps together with realistic city maps from real-world locations and aerial images from open datasources. Initial experiments regarding model architectures, input feature design and estimation of radio maps from aerial images are presented.
  arXiv  Detail & Related papers  (2024-01-12T14:56:45Z)
• Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps [11.327456466796681]
  Estimating path loss for a transmitter-receiver location is key to many use-cases including network planning and handover. We present a transformer-based neural network architecture that enables predicting link-level properties from maps of various dimensions and from sparse measurements.
  arXiv  Detail & Related papers  (2023-10-06T20:17:40Z)
• Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data [37.69303106863453]
  We propose a novel approach for phase-resolved wave surface reconstruction using neural networks. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids.
  arXiv  Detail & Related papers  (2023-05-18T12:30:26Z)
• Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover [87.28082715343896]
  We consider deep neural networks for solving inverse problems that are robust to forward model mis-specifications. We design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem. The proposed REST network is shown to outperform state-of-the-art model-based and data-driven algorithms in both compressive sensing and radar imaging problems.
  arXiv  Detail & Related papers  (2021-10-20T06:15:45Z)
• Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks [7.890819981813062]
  We present a novel method to model radio propagation using deep convolutional neural networks. We also lay down the framework for data-driven modeling of radio propagation.
  arXiv  Detail & Related papers  (2021-10-05T07:20:48Z)
• Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach [6.510061176722248]
  In many trajectory-based applications, it is necessary to map raw GPS trajectories onto road networks in digital maps. In this paper, we consider the map-matching task from the data perspective, proposing a deep learning-based map-matching model. We generate synthetic trajectory data to pre-train the Transformer model and then fine-tune the model with a limited number of ground-truth data.
  arXiv  Detail & Related papers  (2021-08-01T11:51:11Z)
• Probabilistic 3D surface reconstruction from sparse MRI information [58.14653650521129]
  We present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets.
  arXiv  Detail & Related papers  (2020-10-05T14:18:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.