A Real-Time Channel Prediction Model Based on Neural Networks for Dedicated Short-Range Communications
Based on a multiple layer perceptron neural networks, this paper presents a real-time channel prediction model, which could predict channel parameters such as path loss (PL) and packet drop (PD), for dedicated short-range communications (DSRC). The dataset used for training, validating, and testing...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/16/3541 |
| _version_ | 1797999421619699712 |
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| author | Tianhong Zhang Sheng Liu Weidong Xiang Limei Xu Kaiyu Qin Xiao Yan |
| author_facet | Tianhong Zhang Sheng Liu Weidong Xiang Limei Xu Kaiyu Qin Xiao Yan |
| author_sort | Tianhong Zhang |
| collection | DOAJ |
| description | Based on a multiple layer perceptron neural networks, this paper presents a real-time channel prediction model, which could predict channel parameters such as path loss (PL) and packet drop (PD), for dedicated short-range communications (DSRC). The dataset used for training, validating, and testing was extracted from experiments under several different road scenarios including highways, local areas, residential areas, state parks, and rural areas. The study shows that the proposed PL prediction model outperforms conventional empirical models. Meanwhile, the proposed PD prediction model achieves higher prediction accuracy than the statistical one. Moreover, the prediction model can operate in real-time, through updating its training set, to predict channel parameters. Such a model can be easily extended to the applications of autonomous driving, the Internet of Things (IoT), 5th generation cellular network technology (5G) and many others. |
| first_indexed | 2024-04-11T11:04:16Z |
| format | Article |
| id | doaj.art-9b78506d4a844206bc0136ddf6314807 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:04:16Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9b78506d4a844206bc0136ddf63148072022-12-22T04:28:25ZengMDPI AGSensors1424-82202019-08-011916354110.3390/s19163541s19163541A Real-Time Channel Prediction Model Based on Neural Networks for Dedicated Short-Range CommunicationsTianhong Zhang0Sheng Liu1Weidong Xiang2Limei Xu3Kaiyu Qin4Xiao Yan5School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaDepartment of Electrical and Computer Engineering, University of Michigan, Dearborn, MI 48128, USADepartment of Electrical and Computer Engineering, University of Michigan, Dearborn, MI 48128, USASchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaBased on a multiple layer perceptron neural networks, this paper presents a real-time channel prediction model, which could predict channel parameters such as path loss (PL) and packet drop (PD), for dedicated short-range communications (DSRC). The dataset used for training, validating, and testing was extracted from experiments under several different road scenarios including highways, local areas, residential areas, state parks, and rural areas. The study shows that the proposed PL prediction model outperforms conventional empirical models. Meanwhile, the proposed PD prediction model achieves higher prediction accuracy than the statistical one. Moreover, the prediction model can operate in real-time, through updating its training set, to predict channel parameters. Such a model can be easily extended to the applications of autonomous driving, the Internet of Things (IoT), 5th generation cellular network technology (5G) and many others.https://www.mdpi.com/1424-8220/19/16/3541channel modelsneural networkswireless communicationsprediction methodsvehicular and wireless technologiesdedicated short-range communication |
| spellingShingle | Tianhong Zhang Sheng Liu Weidong Xiang Limei Xu Kaiyu Qin Xiao Yan A Real-Time Channel Prediction Model Based on Neural Networks for Dedicated Short-Range Communications Sensors channel models neural networks wireless communications prediction methods vehicular and wireless technologies dedicated short-range communication |
| title | A Real-Time Channel Prediction Model Based on Neural Networks for Dedicated Short-Range Communications |
| title_full | A Real-Time Channel Prediction Model Based on Neural Networks for Dedicated Short-Range Communications |
| title_fullStr | A Real-Time Channel Prediction Model Based on Neural Networks for Dedicated Short-Range Communications |
| title_full_unstemmed | A Real-Time Channel Prediction Model Based on Neural Networks for Dedicated Short-Range Communications |
| title_short | A Real-Time Channel Prediction Model Based on Neural Networks for Dedicated Short-Range Communications |
| title_sort | real time channel prediction model based on neural networks for dedicated short range communications |
| topic | channel models neural networks wireless communications prediction methods vehicular and wireless technologies dedicated short-range communication |
| url | https://www.mdpi.com/1424-8220/19/16/3541 |
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