Related papers: Radio Map Estimation -- An Open Dataset with Directive Transmitter Antennas and Initial Experiments

Radio Map Estimation -- An Open Dataset with Directive Transmitter Antennas and Initial Experiments

URL: http://arxiv.org/abs/2402.00878v1
Date: Fri, 12 Jan 2024 14:56:45 GMT
Title: Radio Map Estimation -- An Open Dataset with Directive Transmitter Antennas and Initial Experiments
Authors: Fabian Jaensch, Giuseppe Caire, Beg\"um Demir
Abstract summary: 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.
Score: 49.61405888107356
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Over the last years, several works have explored the application of deep learning algorithms to determine the large-scale signal fading (also referred to as ``path loss'') between transmitter and receiver pairs in urban communication networks. The central idea is to replace costly measurement campaigns, inaccurate statistical models or computationally expensive ray-tracing simulations by machine learning models which, once trained, produce accurate predictions almost instantly. Although the topic has attracted attention from many researchers, there are few open benchmark datasets and codebases that would allow everyone to test and compare the developed methods and algorithms. We take a step towards filling this gap by releasing a publicly available 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 and the code is made available.

Related papers

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)
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)
Dataset of Pathloss and ToA Radio Maps With Localization Application [59.11388233415274]
The datasets include simulated pathloss/received signal strength ( RSS) and time of arrival ( ToA) radio maps over a large collection of realistic dense urban setting in real city maps. The two main applications of the presented dataset are 1) learning methods that predict the pathloss from input city maps, and, 2) wireless localization. The fact that the RSS and ToA maps are computed by the same simulations over the same city maps allows for a fair comparison of the RSS and ToA-based localization methods.
arXiv Detail & Related papers (2022-11-18T20:39:51Z)
iSDF: Real-Time Neural Signed Distance Fields for Robot Perception [64.80458128766254]
iSDF is a continuous learning system for real-time signed distance field reconstruction. It produces more accurate reconstructions and better approximations of collision costs and gradients.
arXiv Detail & Related papers (2022-04-05T15:48:39Z)
Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs [15.452264020787593]
This paper proposes spectrum surveying, where a mobile robot such as an unmanned aerial vehicle (UAV) collects measurements at a set of locations that are actively selected to obtain high-quality map estimates in a short surveying time. Two novel algorithms, a model-based online Bayesian estimator and a data-driven deep learning algorithm, are devised for updating a map estimate and an uncertainty metric that indicates the informativeness of measurements at each possible location. To overcome the complexity of this problem, a dynamic programming approach is proposed to obtain lists of waypoints through areas of large uncertainty in linear time.
arXiv Detail & Related papers (2022-01-11T18:49:42Z)
Self-supervised Audiovisual Representation Learning for Remote Sensing Data [96.23611272637943]
We propose a self-supervised approach for pre-training deep neural networks in remote sensing. By exploiting the correspondence between geo-tagged audio recordings and remote sensing, this is done in a completely label-free manner. We show that our approach outperforms existing pre-training strategies for remote sensing imagery.
arXiv Detail & Related papers (2021-08-02T07:50:50Z)
Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach [59.17191114000146]
LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task. We show that LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps.
arXiv Detail & Related papers (2021-06-23T17:27:04Z)
Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks [0.0]
Road intersections data have been used across different geospatial applications and analysis. We employed the standard paradigm of using deep convolutional neural network for object detection task named region-based CNN. Also, compared to the majority of traditional computer vision algorithms RCNN provides more accurate extraction.
arXiv Detail & Related papers (2020-07-14T23:51:15Z)

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