A Machine Learning-Assisted Framework for Determination of Performance Degradation Causes and Selection of Channel Switching Strategy in Vehicular Networks [Technology Transfer Activities]

Huang, Chin-tser; Comert, Gurcan; Abuhdima, Esmail; Pisu, Pierluigi; Liu, Jian; Nazeri, Amirhossein

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A Machine Learning-Assisted Framework for Determination of Performance Degradation Causes and Selection of Channel Switching Strategy in Vehicular Networks [Technology Transfer Activities]

2023-02-01
By Huang, Chin-tser ; Comert, Gurcan ; Abuhdima, Esmail

File Language:

English

Details

Alternative Title:

Technology Transfer Activities
Creators:

Huang, Chin-tser ; Comert, Gurcan ; Abuhdima, Esmail ; Pisu, Pierluigi ; Liu, Jian ; Nazeri, Amirhossein
Corporate Creators:

Benedict College ; Clemson University ; University of South Carolina ; Center for Connected Multimodal Mobility, Clemson University
Corporate Contributors:

United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program
Subject/TRT Terms:
Resource Type:

Brief
Geographical Coverage:

United States
Edition:

Technology Transfer
Corporate Publisher:

Center for Connected Multimodal Mobility, Clemson University
Abstract:

We expect this study to add knowledge to the transportation community and the public. Our research suggests that LSTM has the potential to forecast communication degradation in future autonomous vehicle designs. Additionally, our simulation results indicate that the amplitude of linearly polarized electric fields is less affected by dust and sand compared to circularly polarized fields.
Format:

PDF
Funding:

69A3551747117 ; NSF Grant Nos. 1719501 ; 1954532 ; 2131080
Download URL:

https://rosap.ntl.bts.gov/view/dot/74013/dot_74013_DS1.pdf
File Type:

[PDF - 287.79 KB]
Collection(s):

Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
Main Document Checksum:

urn:sha-512:9087d329c2e5c7feb22c9715b44a6d9ef8c5068b9a757f7ad148a35f421271f588f37e644a340cb15d419f2fe169677b1ff940ecbd390e952ef9cd995c64fbe4

A Machine Learning-Assisted Framework for Determination of Performance Degradation Causes and Selection of Channel Switching Strategy in Vehicular Networks [supporting dataset]

Dataset

As all three major US mobile carriers have launched their own 5G networks and are working hard to expand their coverage nationwide, 5G has come into e ...

Huang, Chin Tser ;

Comert, Gurcan

File Language:

English

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