Machine Learning Approaches for Radio Propagation Modeling in Urban Vehicular Channels
The use of vehicular communications is anticipated to improve safety in road traffic. The traditional radio channel models that describe the effects of radio wave propagation in dynamic vehicular environments have their own limitations. In this paper, machine learning (ML) techniques are applied for...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9933738/ |
| _version_ | 1818469484678086656 |
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| author | Khalil Ahmad Sajjad Hussain |
| author_facet | Khalil Ahmad Sajjad Hussain |
| author_sort | Khalil Ahmad |
| collection | DOAJ |
| description | The use of vehicular communications is anticipated to improve safety in road traffic. The traditional radio channel models that describe the effects of radio wave propagation in dynamic vehicular environments have their own limitations. In this paper, machine learning (ML) techniques are applied for radio channel modeling in urban vehicular environments. A large data set of path loss (PL) and root-mean-square Delay spread (RMS-DS) is computed using ray-tracing for a Line-of-Sight (LOS) straight road and a Non-Line-of-Sight (NLOS) intersection road scenario. Fourteen input features are used to train three ML models for vehicular channel prediction. The models considered in this work include Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Random Forest (RF). The results show that RF gives better performance than MLP and CNN models in the prediction of PL and RMS-DS in urban vehicular channels. |
| first_indexed | 2024-04-13T21:25:12Z |
| format | Article |
| id | doaj.art-a59eacc42d1d4a2c9e1fb13d748e0f3b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T21:25:12Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a59eacc42d1d4a2c9e1fb13d748e0f3b2022-12-22T02:29:21ZengIEEEIEEE Access2169-35362022-01-011011369011369810.1109/ACCESS.2022.32186229933738Machine Learning Approaches for Radio Propagation Modeling in Urban Vehicular ChannelsKhalil Ahmad0https://orcid.org/0000-0002-6038-999XSajjad Hussain1https://orcid.org/0000-0001-5390-8766School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, PakistanSchool of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, PakistanThe use of vehicular communications is anticipated to improve safety in road traffic. The traditional radio channel models that describe the effects of radio wave propagation in dynamic vehicular environments have their own limitations. In this paper, machine learning (ML) techniques are applied for radio channel modeling in urban vehicular environments. A large data set of path loss (PL) and root-mean-square Delay spread (RMS-DS) is computed using ray-tracing for a Line-of-Sight (LOS) straight road and a Non-Line-of-Sight (NLOS) intersection road scenario. Fourteen input features are used to train three ML models for vehicular channel prediction. The models considered in this work include Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Random Forest (RF). The results show that RF gives better performance than MLP and CNN models in the prediction of PL and RMS-DS in urban vehicular channels.https://ieeexplore.ieee.org/document/9933738/Vehicular channel modelingmachine learningray tracingmulti-layer perceptronconvolutional neural networkrandom forest |
| spellingShingle | Khalil Ahmad Sajjad Hussain Machine Learning Approaches for Radio Propagation Modeling in Urban Vehicular Channels IEEE Access Vehicular channel modeling machine learning ray tracing multi-layer perceptron convolutional neural network random forest |
| title | Machine Learning Approaches for Radio Propagation Modeling in Urban Vehicular Channels |
| title_full | Machine Learning Approaches for Radio Propagation Modeling in Urban Vehicular Channels |
| title_fullStr | Machine Learning Approaches for Radio Propagation Modeling in Urban Vehicular Channels |
| title_full_unstemmed | Machine Learning Approaches for Radio Propagation Modeling in Urban Vehicular Channels |
| title_short | Machine Learning Approaches for Radio Propagation Modeling in Urban Vehicular Channels |
| title_sort | machine learning approaches for radio propagation modeling in urban vehicular channels |
| topic | Vehicular channel modeling machine learning ray tracing multi-layer perceptron convolutional neural network random forest |
| url | https://ieeexplore.ieee.org/document/9933738/ |
| work_keys_str_mv | AT khalilahmad machinelearningapproachesforradiopropagationmodelinginurbanvehicularchannels AT sajjadhussain machinelearningapproachesforradiopropagationmodelinginurbanvehicularchannels |