eprintid: 20454
rev_number: 3
eprint_status: archive
userid: 1
dir: disk0/00/02/04/54
datestamp: 2026-01-29 06:27:51
lastmod: 2026-01-29 06:27:51
status_changed: 2026-01-29 06:27:51
type: conference_item
metadata_visibility: show
creators_name: Bakhuraisa, Yaser A.
creators_name: Abd.aziz, Azlan B.
creators_name: Geok, Tan K.
creators_name: Bakar, Norazhar B. Abu
creators_name: Jamian, Saifulnizan B.
title: Mm-Wave RSS Evaluation for Distance Estimation in Urban Environments
ispublished: pub
keywords: Millimeter waves; Urban planning; 28 GHz; Accurate estimation; Distance estimation; LOS; Mm waves; NLOS; Path loss; Received signal strength; Strength models; Urban environments; Ray tracing
note: Cited by: 0
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
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149141758&doi=10.1109%2fICFTSC57269.2022.10040059&partnerID=40&md5=2e45cc18d7c15cdf20fd4c784c7c4846
id_number: 10.1109/ICFTSC57269.2022.10040059
full_text_status: none
publication: 2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022
pagerange: 141 – 146
refereed: TRUE
isbn: 979-835033454-8
citation:   Bakhuraisa, Yaser A. and Abd.aziz, Azlan B. and Geok, Tan K. and Bakar, Norazhar B. Abu and Jamian, Saifulnizan B.  (2022) Mm-Wave RSS Evaluation for Distance Estimation in Urban Environments.  In: UNSPECIFIED.