Path Loss Exponent and Shadowing Factor Prediction From Satellite Images Using Deep Learning

Ates, Hasan F.; Hashir, Syed Muhammad; Baykaş, Tunçer; Güntürk, Bahadir K.

doi:10.1109/access.2019.2931072

Cited by 57 publications

(58 citation statements)

References 23 publications

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“…For our task, it is not feasible to do actual measurements to cover a variety of scenarios. Therefore, we follow the process of using 3D models and ray tracing simulations to generate the dataset [25]. (While some parts of the process are identical to those of [25], we write all the steps here for completeness.)…”

Section: Dataset Generationmentioning

confidence: 99%

“…In a recent study [24], deep learning model is applied on real measurements with the aid of satellite images and input features to predict the RSRP for specific receiver locations in a limited area/scenario. Instead, in [25], channel parameters (e.g., path loss exponent and standard deviation of shadowing) are estimated directly from satellite images using deep learning without the need of any additional input features and for different area types and scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…The regression network is based on the VGG-16 architecture, whose pretrained weights are used for initialization before training. In a previous work [25], we also used deep learning for path loss model parameter prediction. The main difference between [25] and this work is that, in [25] we predict two model parameters, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…In a previous work [25], we also used deep learning for path loss model parameter prediction. The main difference between [25] and this work is that, in [25] we predict two model parameters, i.e. path loss exponent and shadowing factor.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we predict the overall distribution of path loss. In [25], path loss exponent and shadowing factor parameters are quantized, forming a discrete set of classes; hence, the network is constructed as a classifier network. In this work, the path loss distribution is divided into bins to form a histogram; and the network is constructed as a regression network.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Path loss prediction is essential for network planning in any wireless communication system. For cellular networks, it is usually achieved through extensive received signal power measurements in the target area. When the 3D model of an area is available, ray tracing simulations can be utilized; however, an important drawback of such an approach is the high computational complexity of the simulations. In this paper, we present a fundamentally different approach for path loss distribution prediction directly from 2D satellite images based on deep convolutional neural networks. While training process is time consuming and completed offline, inference can be done in real time. Another advantage of the proposed approach is that 3D model of the area is not needed during inference since the network simply uses an image captured by an aerial vehicle or satellite as its input. Simulation results show that the path loss distribution can be accurately predicted for different communication frequencies and transmitter heights. INDEX TERMS Path loss, deep learning, convolutional neural networks.

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Section: Dataset Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Machine Learning and Deep Learning powered satellite communications: Enabling technologies, applications, open challenges, and future research directions

Bhattacharyya

Nambiar

Ojha

et al. 2023

Satell Commun Network

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SummaryThe recent wave of creating an interconnected world through satellites has renewed interest in satellite communications. Private and government‐funded space agencies are making advancements in the creation of satellite constellations, and the introduction of 5G has brought a new focus to a fully connected world. Satellites are the proposed solutions for establishing high throughput and low latency links to remote, hard‐to‐reach areas. This has caused the injection of many satellites in Earth's orbit, which has caused many discrepancies. There is a need to establish highly adaptive and flexible satellite systems to overcome this. Machine Learning (ML) and Deep Learning (DL) have gained much popularity when it comes to communication systems. This review extensively provides insight into ML and DL's utilization in satellite communications. This review covers how satellite communication subsystems and other satellite system applications can be implemented through Artificial Intelligence (AI) and the ongoing open challenges and future directions.

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Deep learning for both broadband prediction of the radiated emission from heatsinks and heatsink optimization

Başyiğit

Genç

Doğan³

et al. 2021

Engineering Science and Technology, an International Journal

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Path Loss Exponent and Shadowing Factor Prediction From Satellite Images Using Deep Learning

Cited by 57 publications

References 23 publications

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

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

Machine Learning and Deep Learning powered satellite communications: Enabling technologies, applications, open challenges, and future research directions

Deep learning for both broadband prediction of the radiated emission from heatsinks and heatsink optimization

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