Land use classification as a key component for path loss prediction in rural areas

Neunerdt, Melanie; Engels, Alexander; Mathar, Rudolf

doi:10.1109/iswcs.2010.5624542

Cited by 7 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The choice of feature set is critical; and various other features, such as transmitter/receiver heights, antenna seperation, transmitting frequency, mean building height and road width have been proposed [12]- [18]. Aside from urban areas, it is also possible to classify the target region from the aerial images into different classes such as forest or village, and use a suitable path loss model [19]. Recently in [20], a survey of existing machine learning methods in literature for path loss predic-tion, including decision tree based [21] and support vector regression based [22] methods, is presented.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Their accuracy is sufficient and of equal value or better than respective ANN methodologies proposed in the litterature. Indicatively, in [20] statistical results for MAE, (4 dB-6 dB) are presented and also in [10], (2.65 dB-6.12 dB), in [12], (3.67 dB-5.04 dB) and in [14], (3.3 dB-5.1 dB) have been obtained.…”

Section: General Conclusionmentioning

confidence: 78%

Mobile radio propagation path loss prediction using Artificial Neural Networks with optimal input information for urban environments

Sotiroudis

Siakavara

2015

AEU - International Journal of Electronics and Communications

View full text Add to dashboard Cite

“…In recent years, the use of machine learning techniques, including deep neural networks, in wireless communication applications has become increasingly popular [12]. Machine learning based methods have been proposed to estimate path loss [13], classify the area model selection [14], and allocate resources [15]. Deep neural networks have been used to predict path loss exponent and shadowing factor [16] and path loss distribution [17].…”

Section: Introductionmentioning

confidence: 99%

Predicting Path Loss Distributions of a Wireless Communication System for Multiple Base Station Altitudes from Satellite Images

Shoer

Güntürk

Ates

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

It is expected that unmanned aerial vehicles (UAVs) will play a vital role in future communication systems. Optimum positioning of UAVs, serving as base stations, can be done through extensive field measurements or ray tracing simulations when the 3D model of the region of interest is available. In this paper, we present an alternative approach to optimize UAV base station altitude for a region. The approach is based on deep learning; specifically, a 2D satellite image of the target region is input to a deep neural network to predict path loss distributions for different UAV altitudes. The neural network is designed and trained to produce multiple path loss distributions in a single inference; thus, it is not necessary to train a separate network for each altitude.

show abstract

Land use classification as a key component for path loss prediction in rural areas

Cited by 7 publications

References 6 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

Mobile radio propagation path loss prediction using Artificial Neural Networks with optimal input information for urban environments

Predicting Path Loss Distributions of a Wireless Communication System for Multiple Base Station Altitudes from Satellite Images

Contact Info

Product

Resources

About