TY - CHAP
T1 - VLC Indoor Positioning Using RFR and SVM Reduced Features Machine Learning Techniques
AU - Affan, Affan
AU - Asif, Hafiz M.
AU - Tarhuni, Naser
N1 - Publisher Copyright:
© 2023 IEEE.
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PY - 2023/4/19
Y1 - 2023/4/19
N2 - Artificial intelligence algorithms require large datasets for better performance for all kinds of tasks such as classification and regression. In this paper, we explore the potential of the Random Forest Regression (RFR) algorithm and Support Vector Machine (SVM) algorithm with minimum features, such as signal power and its variants, for Visible Light Communication (VLC) based indoor positioning. We explore the performance of the RFR algorithm and SVM by using variations of the received signal power to increase the accuracy and reduce the computation complexity. The simulation results demonstrate that both techniques have estimated the location with high accuracy, however, RFR outperforms SVM in terms of mean error.
AB - Artificial intelligence algorithms require large datasets for better performance for all kinds of tasks such as classification and regression. In this paper, we explore the potential of the Random Forest Regression (RFR) algorithm and Support Vector Machine (SVM) algorithm with minimum features, such as signal power and its variants, for Visible Light Communication (VLC) based indoor positioning. We explore the performance of the RFR algorithm and SVM by using variations of the received signal power to increase the accuracy and reduce the computation complexity. The simulation results demonstrate that both techniques have estimated the location with high accuracy, however, RFR outperforms SVM in terms of mean error.
KW - Channel Model
KW - Random Forest Regression
KW - Simulation
KW - Support Vector Machine
KW - Visible Light Communication
UR - http://www.scopus.com/inward/record.url?scp=85161268624&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85161268624&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/dd9ab7c8-5742-3fd7-9596-10e6dfc1dd9d/
U2 - 10.1109/wts202356685.2023.10131742
DO - 10.1109/wts202356685.2023.10131742
M3 - Chapter
AN - SCOPUS:85161268624
SN - 9781665490924
T3 - Wireless Telecommunications Symposium
SP - 1
EP - 6
BT - 2023 Wireless Telecommunications Symposium, WTS 2023
PB - IEEE Computer Society
T2 - 22nd Wireless Telecommunications Symposium, WTS 2023
Y2 - 19 April 2023 through 21 April 2023
ER -