{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:44:10Z","timestamp":1760240650988,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,13]],"date-time":"2019-08-13T00:00:00Z","timestamp":1565654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871093"],"award-info":[{"award-number":["61871093"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s19163541","type":"journal-article","created":{"date-parts":[[2019,8,14]],"date-time":"2019-08-14T03:59:26Z","timestamp":1565755166000},"page":"3541","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Real-Time Channel Prediction Model Based on Neural Networks for Dedicated Short-Range Communications"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6722-2853","authenticated-orcid":false,"given":"Tianhong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Sheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Michigan, Dearborn, MI 48128, USA"}]},{"given":"Weidong","family":"Xiang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Michigan, Dearborn, MI 48128, USA"}]},{"given":"Limei","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Kaiyu","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Xiao","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1109\/TITS.2018.2803628","article-title":"Composite \u03b1 \u2212 \u03bc Based DSRC Channel Model Using Large Data Set of RSSI Measurements","volume":"20","author":"Mahjoub","year":"2018","journal-title":"IEEE Trans. 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