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primary topic: Mm-Wave RSS Evaluation for Distance Estimation in Urban Environments

Mm-Wave RSS Evaluation for Distance Estimation in Urban Environments
type: AcademicArticle
type: Article
abstract: In the recent years, mm-wave bands have become popular in the modern wireless communication and vehicular positioning. However, accurate estimation of the distance between the base station and the vehicle is very important to improve localization accuracy. In this work, we evaluated the accuracy of the distance estimation based on the received signal strength (RSS) model for 28 GHz mm-wave in urban environments with LOS and NLOS scenarios. Ray tracing method have been used to predict the RSS of the aforementioned frequency band. The parameters of path loss model, i.e., Close In Log-Distance (CILD) Model, are derived based on linear regression of predicted RSS. The results showed that, RSS model have provided an acceptable level of distance estimation. It provided more accurate estimation in the LOS scenario compared to NLOS scenario. The correlation coefficients (R2) between the actual distance and the estimated distance were 0.76 and 0.73 for LOS and NOLS scenarios respectively. The mean absolute error for distance estimation was 3. S23m in LOS, while 4. SS7m was obtained for NLOS. © 2022 IEEE.
date: 2022
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ConferenceItemEPrint
is part of: repository

Institute of Electrical and Electronics Engineers Inc.
type: Organization

2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022
type: Collection

Norazhar B. Abu Bakar
type: Person
given name: Norazhar B. Abu
family name: Bakar

Saifulnizan B. Jamian
type: Person
given name: Saifulnizan B.
family name: Jamian

Azlan B. Abd.aziz
type: Person
given name: Azlan B.
family name: Abd.aziz

Tan K. Geok
type: Person
given name: Tan K.
family name: Geok

Yaser A. Bakhuraisa
type: Person
given name: Yaser A.
family name: Bakhuraisa

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primary topic: Mm-Wave RSS Evaluation for Distance Estimation in Urban Environments