Using Learning Methods for V2V Path Loss Prediction

Ramya, P.; Boban, Mate; Zhou, Chan; Stańczak, Sławomir

doi:10.1109/wcnc.2019.8885783

Cited by 22 publications

(17 citation statements)

References 8 publications

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“…Accordingly, researchers have accelerated their efforts to provide reliable models for various environments and scenarios over a wide range of frequency regimes to assist network engineers in designing reliable future wireless networks and accurate link budget calculations. Moreover, accurate predictions could be beneficial in radio resource management schemes that aim to meet specific Quality-of-Service (QoS) criteria [13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…Path loss prediction models based on machine learning algorithms are promising to overcome the time consumption in traditional linear path loss models that depend mainly on measurement campaigns at new frequency bands in specific outdoor and indoor environments and communication scenarios and/or simulation-based methods, such as ray-tracing techniques [6]. ML-based algorithms have been successfully used to assist to predict the path loss in several operating environments [6,7,11,13,21,. Furthermore, unlike traditional models, ML-based path loss prediction models can provide reliable generalizations on the propagation environment [42].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels

Elmezughi

Salih

Afullo

et al. 2022

Sensors

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Unlimited access to information and data sharing wherever and at any time for anyone and anything is a fundamental component of fifth-generation (5G) wireless communication and beyond. Therefore, it has become inevitable to exploit the super-high frequency (SHF) and millimeter-wave (mmWave) frequency bands for future wireless networks due to their attractive ability to provide extremely high data rates because of the availability of vast amounts of bandwidth. However, due to the characteristics and sensitivity of wireless signals to the propagation effects in these frequency bands, more accurate path loss prediction models are vital for the planning, evaluating, and optimizing future wireless communication networks. This paper presents and evaluates the performance of several well-known machine learning methods, including multiple linear regression (MLR), polynomial regression (PR), support vector regression (SVR), as well as the methods using decision trees (DT), random forests (RF), K-nearest neighbors (KNN), artificial neural networks (ANN), and artificial recurrent neural networks (RNN). RNNs are mainly based on long short-term memory (LSTM). The models are compared based on measurement data to provide the best fitting machine-learning-based path loss prediction models. The main results obtained from this study show that the best root-mean-square error (RMSE) performance is given by the ANN and RNN-LSTM methods, while the worst is for the MLR method. All the RMSE values for the given learning techniques are in the range of 0.0216 to 2.9008 dB. Furthermore, this work shows that the models (except for the MLR model) perform excellently in fitting actual measurement data for wireless communications in enclosed indoor environments since they provide R-squared and correlation values higher than 0.91 and 0.96, respectively. The paper shows that these learning methods could be used as accurate and stable models for predicting path loss in the mmWave frequency regime.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels

Elmezughi

Salih

Afullo

et al. 2022

Sensors

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“…Recently, machine learning (ML) methods are proposed for channel modelling to overcome site-specific, high complexity limitations of deterministic approaches and low accuracy limitations of stochastic models [2]. Moreover, highly complicated mediums such as in-body, underwater, vehicle to vehicle (V2V) [3], optical and molecular communication channels [4], inherent certain distortion effects which are challenging to be expressed analytically. Therefore, ML based channel modelling aims to develop low-complexity and accurate models for complicated channels, through direct learning of the robust patterns in the data without imposing any assumptions on the analytical expressions.…”

Section: Introductionmentioning

confidence: 99%

“…To date, ML based OWC channel modelling has not been investigated in the literature. However, ML based channel model frameworks trained through measurement data sets, on contrary to relying numerous assumptions are proposed for RF communications [3], [4], [25]- [30]. Considering millimeterwave (mmWave) communication channels, Huang et.al.…”

Section: Introductionmentioning

confidence: 99%

“…For vehicular communication channels, authors in [30] proposed an ANN based channel model outperforming generalized gamma, polynomial fitting and dual slope distance-break point models for path loss predictions. Ramya et.al showed that non-parametric learning based Random Forest method, increased V2V channel path loss prediction accuracy with 2.2 dB mean and 1.5 dB standard deviation of absolute error when compared to log distance path loss model [3]. On the other hand, [4] proposed an ANN based molecular communications channel model to predict channel model parameters accurately, where obtaining exact analytical channel model is challenging.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Based Channel Modeling for Vehicular Visible Light Communication

Turan¹,

Coleri²

2020

Preprint

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Optical Wireless Communication (OWC) propagation channel characterization plays a key role on the design and performance analysis of Vehicular Visible Light Communication (VVLC) systems. Current OWC channel models based on deterministic and stochastic methods, fail to address mobility induced ambient light, optical turbulence and road reflection effects on channel characterization. Therefore, alternative machine learning (ML) based schemes, considering ambient light, optical turbulence, road reflection effects in addition to intervehicular distance and geometry, are proposed to obtain accurate VVLC channel loss and channel frequency response (CFR). This work demonstrates synthesis of ML based VVLC channel model frameworks through multi layer perceptron feed-forward neural network (MLP), radial basis function neural network (RBF-NN) and Random Forest ensemble learning algorithms. Predictor and response variables, collected through practical road measurements, are employed to train and validate proposed models for various conditions. Additionally, the importance of different predictor variables on channel loss and CFR is assessed, normalized importance of features for measured VVLC channel is introduced. We show that RBF-NN, Random Forest and MLP based models yield more accurate channel loss estimations with 3.53 dB, 3.81 dB, 3.95 dB root mean square error (RMSE), respectively, when compared to fitting curve based VVLC channel model with 7 dB RMSE. Moreover, RBF-NN and MLP models are demonstrated to predict VVLC CFR with respect to distance, ambient light and receiver inclination angle predictor variables with 3.78 dB and 3.60 dB RMSE respectively.

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Path loss modeling based on neural networks and ensemble method for future wireless networks

Elmezughi,

Salih,

Afullo

et al. 2023

Heliyon

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Using Learning Methods for V2V Path Loss Prediction

Cited by 22 publications

References 8 publications

Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels

Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels

Machine Learning Based Channel Modeling for Vehicular Visible Light Communication

Path loss modeling based on neural networks and ensemble method for future wireless networks

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