Predicting Path Loss Distribution of an Area From Satellite Images Using Deep Learning

Ahmadien, Omar; Ates, Hasan F.; Baykaş, Tunçer; Güntürk, Bahadir K.

doi:10.1109/access.2020.2985929

Cited by 74 publications

(59 citation statements)

References 37 publications

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“…He states that 1 dB to 4.7 dB improvement factor in path loss prediction with his path loss model compared to empirical models. Likewise, the research conducted by Ahmadien et al demonstrates a path loss model with Kmean clustering, deep learning techniques, and 3D images that converted from 2D satellite images via various simulation software [16]. Although 2D images are insufficient to create 3D images in many cases, he proposed a simulation-based path loss model in his study with limited parameters.…”

Section: Related Workmentioning

confidence: 99%

3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile Communication Systems

Egi¹,

Eyceyurt²

2022

Data Science, Data Visualization, and Digital Twins

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Mobile communication is one of the most important parameters of smart cities in terms of maintaining connectivity and interaction between humans and smart systems. However, In the deployment process of Mobile Communication Systems (MCS), Radio Frequency (RF) engineers use location depended empirical Signal Strength Path Loss (SSPL) models ending up with poor signal strength and slow data connection. This is due to the fact that empirical propagation models usually are restrained by the environment and do not implement state of the art technologies, including Unmanned Aerial Vehicles (UAV), Light Detection and Ranging (LiDAR), Image Processing, and Machine Learning to increase efficiency. Terrains involving buildings, hills, trees, mountains, and human-made structures are considered irregular terrains by telecommunication engineers. Irregular terrains, specifically trees, significantly affect MCS’s efficiency because of their complex pattern resulting in erroneous signal fading via multi-path reflection and absorption. Therefore, a virtual 3D environment is required to extract the required 3D terrain pattern and elevation data from the environment. Once this data is processed in the machine learning algorithm, an adaptive propagation model can be formed and can significantly improve SSPL prediction accuracy for MCS. This chapter presents 3D point cloud visualization via sensor fusion and 2D image color classification techniques, which lead to a novel propagation model for the smart deployment of MCS. The proposed system’s main contribution is to develop an intelligent environment that eliminates limitations and minimizes related signal fading prediction errors. In addition, having better connectivity and efficiency will resolve the communication problem of smart cities. The chapter also provides a case study that significantly outperforms other empirical models with an accuracy of 95.4%.

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Section: Related Workmentioning

confidence: 99%

3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile Communication Systems

Egi¹,

Eyceyurt²

2022

Data Science, Data Visualization, and Digital Twins

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“…Instead, the use of simulated data can enlarge training datasets at a cost that is significantly smaller to that of a measurement campaign. Thus, data generated by physicsbased methods, such as RT [14], [25]- [27], or other sources of data that are readily available (like satellite images in outdoor environments [28], [29]) have been used to train ML models. In indoor environments, where RT-based training is more extensively used, the availability of synthetic data offers more possibilities for geometry generalization.…”

Section: Ml-based Propagation Modelsmentioning

confidence: 99%

Towards Physics-Based Generalizable Convolutional Neural Network Models for Indoor Propagation

Seretis¹

2021

Preprint

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A fundamental challenge for machine learning models for electromagnetics is their ability to predict output quantities of interest (such as fields and scattering parameters) in geometries that the model has not been trained for. Addressing this challenge is a key to fulfilling one of the most appealing promises of machine learning for computational electromagnetics: the rapid solution of problems of interest just by processing the geometry and the sources involved. The impact of such models that can "generalize" to new geometries is more profound for large-scale computations, such as those encountered in wireless propagation scenarios. We present generalizable models for indoor propagation that can predict received signal strengths within new geometries, beyond those of the training set of the model, for transmitters and receivers of multiple positions, and for new frequencies. We show that a convolutional neural network can "learn" the physics of indoor radiowave propagation from ray-tracing solutions of a small set of training geometries, so that it can eventually deal with substantially different geometries. We emphasize the role of exploiting physical insights in the training of the network, by defining input parameters and cost functions that assist the network to efficiently learn basic and complex propagation mechanisms.

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“…In drive test campaigns, sophisticated software and measuring tools are used to measure the propagation characteristics. Presently, ray tracing techniques have been extended to model ANN and CNN-based path loss prediction models by combining 3D models with 2D satellite imagery to develop generic path loss prediction methods [26], [27], [28], [29] and [30]. While considering 3D digital maps, a given map's accuracy depends on how the terrain, foliage, and city buildings are portrayed; also captured detailed features like building footprints, building edges, facades, street features, vertical and horizontal, and the transmitting and receiving antennas position.…”

Section: Background and Related Workmentioning

confidence: 99%

Deterministic 5G mmWave Large-Scale 3D Path Loss Model for Lagos Island, Nigeria

Hinga

Atayero

2021

IEEE Access

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5G millimeter wave (mmWave) application in mobile connectivity to realize high-speed, reliable communication is attributed with high path loss. This paper presents a detailed 3D ray-tracing technique at 28 GHz for Lagos Island to investigate five unique path loss scenarios: path loss, free space path loss with antenna pattern, free space path loss without antenna pattern, excess path loss with antenna pattern, and the excess path loss without antenna pattern for an urban environment. The Close-In (CI) model, Floating Intercept (FI) path loss model, and a root mean square error (RMSE) are used to model and evaluate the best path loss model for Lagos Island. The average achieved FI (α ,β, σ) parameters were 189.92352, 0.1654, and 0.66948, While the average CI (η,Xσ) parameters were 2.309355 and 56.236425. From all the scenarios evaluated, the lowest path loss exponent achieved was 0.45, while the highest path loss exponent was 3.8. We have established that the FI path loss model accurately characterizes path loss for the Lagos Island environment with the lowest RMSE of 0.0359 dB and the highest RSME of 0.0997 dB. In contrast, the CI model over-predict the path loss at 28 GHz with the lowest RMSE of 0.0495 dB and the highest RMSE of 2.2547 dB. This work opens up a new area of research on mm-Wave at 28 GHz in Lagos Island, and the results obtained from this work can be used to benchmark future studies on mmWave in a similar environment.

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Predicting Path Loss Distribution of an Area From Satellite Images Using Deep Learning

Cited by 74 publications

References 37 publications

3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile Communication Systems

3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile Communication Systems

Towards Physics-Based Generalizable Convolutional Neural Network Models for Indoor Propagation

Deterministic 5G mmWave Large-Scale 3D Path Loss Model for Lagos Island, Nigeria

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