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

Sotiroudis, Sotirios P.; Siakavara, Katherine

doi:10.1016/j.aeue.2015.06.014

Cited by 44 publications

(22 citation statements)

References 19 publications

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“…The network can then be used as a standalone propagation modeling engine and used to obtain path loss parameters in unfamiliar scenarios. Previous work on the use of NN techniques in propagation modeling as well as other applications in wireless networking can be found in [15][16][17][18][19][20]. However, the authors were unable to locate a systematic study of the application of neural network techniques specifically to UWB propagation modeling for the implant environment in the existing literature.…”

Section: Introductionmentioning

confidence: 90%

A new propagation modeling technique for ultra-wideband implant body area networks based on a neural network architecture

Kanaan

Suveren

2016

Neural Comput & Applic

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We report on the results of our investigations into more computationally efficient methods for propagation modeling in the implant environment through the use of neural networks. Our results are applicable to the important case of ultra-wideband systems operating in the implant environment. A propagation modeling engine based on neural networks can help the system designer avoid time-consuming numerical simulations normally utilized for propagation modeling. The contributions of this paper are as follows. We perform a systematic investigation of two neural network architectures for propagation modeling, namely the multilayer perceptron (MLP) and the recurrent Elman network. We perform a quantitative comparison of the results produced by these networks to the results produced by time-consuming numerical methods, such as the finite difference time domain (FDTD) method. Our results indicate that it is indeed possible to obtain path loss results using neural network methods that approach FDTD results within a 3-to 4-dB margin of error. We further illustrate that it is possible to attain this level of accuracy through the simpler MLP structure, and that the increased complexity and training time associated with the Elman network is unwarranted. We also observe that the MLP network can attain this performance through reasonable network sizes of 20-30 neurons.

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Section: Introductionmentioning

confidence: 90%

A new propagation modeling technique for ultra-wideband implant body area networks based on a neural network architecture

Kanaan

Suveren

2016

Neural Comput & Applic

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“…The need for accurate predictions of propagation path loss has driven the research among various machine learning algorithms [1][2][3][4][5][6][7][8][9]. Substantial efforts have been made in order to optimally define their internal configuration [10][11][12], in an attempt to achieve the best results possible.…”

Section: Related Workmentioning

confidence: 99%

“…Those of the former category can be said to belong in the "classical" machine learning domain and implement techniques such as Support Vector Machines [1], Multilayer Perceptron Neural Networks [2], KNN [3], Random Forest [4], ANFIS [5] etc. This category of models uses data of tabular format at their inputs.…”

Section: Introductionmentioning

confidence: 99%

Fusing Diverse Input Modalities for Path Loss Prediction: A Deep Learning Approach

et al. 2021

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Tabular data and images have been used from machine learning models as two diverse types of inputs, in order to perform path loss predictions in urban areas. Different types of models are applied on these distinct modes of input information. The work at hand tries to incorporate both modes of input data within a single prediction model. It therefore manipulates and transforms the vectors of tabular data into images. Each feature of the tabular data vector is spread into several pixels, corresponding to the calculated importance of the particular feature. The resulting synthetic images are then fused with images representing selected regions of the area's map. Compound pseudoimages, having channels of both map-based and tabular data-based images, are then being used as inputs for a Convolutional Neural Network (CNN), which predicts the path loss value at a specific point of the area of interest. The results are clearly better than those obtained from models based on a single mode of input data, as well as from the results produced by other bimodal-input approaches. This approach could be applied for path loss prediction in urban environments for several state-of-art wireless networks like 5G and Internet of Things (IoT).

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“…Artificial neural networks (ANN) such as the support vector machine (SVM) and back propagation neural network (BPNN) has been widely employed to solve regression problems [18][19][20][21][22]. ELM is a new and efficient method to train a single-hidden-layer feedforward neural networks (SLFNs) [23].…”

Section: Elm-assisted Toa Estimationmentioning

confidence: 99%

A novel time of arrival estimation algorithm using an energy detector receiver in MMW systems

Liang

Zhang

Lyu

et al. 2017

EURASIP J. Adv. Signal Process.

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This paper presents a new time of arrival (TOA) estimation technique using an improved energy detection (ED) receiver based on the empirical mode decomposition (EMD) in an impulse radio (IR) 60 GHz millimeter wave (MMW) system. A threshold is employed via analyzing the characteristics of the received energy values with an extreme learning machine (ELM). The effect of the channel and integration period on the TOA estimation is evaluated. Several well-known ED-based TOA algorithms are used to compare with the proposed technique. It is shown that this ELM-based technique has lower TOA estimation error compared to other approaches and provides robust performance with the IEEE 802.15.3c channel models.

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

Cited by 44 publications

References 19 publications

A new propagation modeling technique for ultra-wideband implant body area networks based on a neural network architecture

A new propagation modeling technique for ultra-wideband implant body area networks based on a neural network architecture

Fusing Diverse Input Modalities for Path Loss Prediction: A Deep Learning Approach

A novel time of arrival estimation algorithm using an energy detector receiver in MMW systems

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