GWRF: A Generalizable Wireless Radiance Field for Wireless Signal Propagation Modeling

• URL: http://arxiv.org/abs/2502.05708v1
• Date: Sat, 08 Feb 2025 22:03:08 GMT
• Title: GWRF: A Generalizable Wireless Radiance Field for Wireless Signal Propagation Modeling
• Authors: Kang Yang, Yuning Chen, Wan Du,
• Abstract summary: Generalizable Wireless Radiance Fields (GWRF) is a framework for modeling wireless signal propagation at arbitrary 3D transmitter and receiver positions. First, a geometry-aware Transformer encoder-based wireless scene representation module incorporates information from geographically proximate transmitters to learn a generalizable wireless radiance field. Second, a neural-driven ray tracing algorithm operates on this field to automatically compute signal reception at the receiver.
• Score: 5.744904421002954
• License:
• Abstract: We present Generalizable Wireless Radiance Fields (GWRF), a framework for modeling wireless signal propagation at arbitrary 3D transmitter and receiver positions. Unlike previous methods that adapt vanilla Neural Radiance Fields (NeRF) from the optical to the wireless signal domain, requiring extensive per-scene training, GWRF generalizes effectively across scenes. First, a geometry-aware Transformer encoder-based wireless scene representation module incorporates information from geographically proximate transmitters to learn a generalizable wireless radiance field. Second, a neural-driven ray tracing algorithm operates on this field to automatically compute signal reception at the receiver. Experimental results demonstrate that GWRF outperforms existing methods on single scenes and achieves state-of-the-art performance on unseen scenes.

Related papers

• Rydberg Atomic Quantum Receivers for Classical Wireless Communications and Sensing: Their Models and Performance [78.76421728334013]
  Rydberg atomic quantum receivers (RAQRs) are an eminent solution for detecting the electric field of radio frequency (RF) signals. We introduce the superheterodyne version of RAQRs to the wireless community by presenting an end-to-end reception scheme. We then develop a corresponding equivalent baseband signal model relying on a realistic reception flow.
  arXiv  Detail & Related papers  (2024-12-07T06:25:54Z)
• WRF-GS: Wireless Radiation Field Reconstruction with 3D Gaussian Splatting [8.644949917126755]
  We present WRF-GS, a novel framework for channel modeling based on wireless radiation field (WRF) reconstruction using 3D Gaussian splatting. With a small number of measurements, WRF-GS can synthesize new spatial spectra within milliseconds for a given scene. WRF-GS achieves superior performance in the channel state information prediction task, surpassing existing methods by a significant margin of more than 2.43 dB.
  arXiv  Detail & Related papers  (2024-12-06T07:56:14Z)
• RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments [1.7074276434401858]
  We introduce a novel machine learning-empowered methodology for wireless channel modeling. Key ingredients include a point-cloud-based neural network and a Spherical Harmonics encoder with light probes.
  arXiv  Detail & Related papers  (2024-06-04T01:06:41Z)
• Enabling Visual Recognition at Radio Frequency [13.399148413043411]
  PanoRadar is a novel RF imaging system that brings RF resolution close to that of LiDAR. Results enable, for the first time, a variety of visual recognition tasks at radio frequency. Our results demonstrate PanoRadar's robust performance across 12 buildings.
  arXiv  Detail & Related papers  (2024-05-29T20:52:59Z)
• NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections [57.63028964831785]
  Recent works have improved NeRF's ability to render detailed specular appearance of distant environment illumination, but are unable to synthesize consistent reflections of closer content. We address these issues with an approach based on ray tracing. Instead of querying an expensive neural network for the outgoing view-dependent radiance at points along each camera ray, our model casts rays from these points and traces them through the NeRF representation to render feature vectors.
  arXiv  Detail & Related papers  (2024-05-23T17:59:57Z)
• Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation [51.346733271166926]
  Mesh2NeRF is an approach to derive ground-truth radiance fields from textured meshes for 3D generation tasks. We validate the effectiveness of Mesh2NeRF across various tasks.
  arXiv  Detail & Related papers  (2024-03-28T11:22:53Z)
• Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing [74.12670841657038]
  Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. Data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. We propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
  arXiv  Detail & Related papers  (2023-12-08T13:50:30Z)
• NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions [93.02515761070201]
  We present a novel type of neural fields that uses general radial bases for signal representation. Our method builds upon general radial bases with flexible kernel position and shape, which have higher spatial adaptivity and can more closely fit target signals. When applied to neural radiance field reconstruction, our method achieves state-of-the-art rendering quality, with small model size and comparable training speed.
  arXiv  Detail & Related papers  (2023-09-27T06:32:05Z)
• Three-Way Deep Neural Network for Radio Frequency Map Generation and Source Localization [67.93423427193055]
  Monitoring wireless spectrum over spatial, temporal, and frequency domains will become a critical feature in beyond-5G and 6G communication technologies. In this paper, we present a Generative Adversarial Network (GAN) machine learning model to interpolate irregularly distributed measurements across the spatial domain.
  arXiv  Detail & Related papers  (2021-11-23T22:25:10Z)
• ChaRRNets: Channel Robust Representation Networks for RF Fingerprinting [0.0]
  We present complex-valued Convolutional Neural Networks (CNNs) for RF fingerprinting. We focus on the problem of fingerprinting wireless IoT devices in-the-wild using Deep Learning (DL) techniques.
  arXiv  Detail & Related papers  (2021-05-08T03:03:21Z)

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.