@inproceedings{scholars20451,
           pages = {324 - 329},
            year = {2022},
             doi = {10.1109/ICDI57181.2022.10007293},
           title = {mm-Wave RSS Evaluation for Distance Estimation in Urban Environments},
         journal = {2022 International Conference on Digital Transformation and Intelligence, ICDI 2022 - Proceedings},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
            note = {Cited by: 1},
            isbn = {979-835039700-0},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146978017&doi=10.1109\%2fICDI57181.2022.10007293&partnerID=40&md5=fc917752ee313e639ff3871c303c0ec2},
          author = {Bakhuraisa, Yaser A. and Aziz, Azlan B. Abd. and Geok, Tan K. and Abu Bakar, Norazhar B. and Jamian, Saifulnizan B.},
        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},
        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 3.823 m in LOS, while 4.887 m was obtained for NLOS. {\copyright} 2022 IEEE.}
}