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Estimation of Received Signal Power for 5G-Railway Communication Systems
Lukman S.a, Nazaruddin Y.Y.a, Ai B.b, He R.b, Joelianto E.a
a Institut Teknologi Bandung, Program of Engineering Physics, Faculty of Industrial Technology, Bandung, Indonesia
b Jiaotong University, State Key Laboratory of Rail Traffic Control and Safety Beijing, Beijing, China
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.This paper presents the estimation of received power signal based on Support Vector Regression (SVR). The simulated datasets are used, which contain the positions of transmitter (Tx) and receiver (Rx), the distance of TX and RX, and corresponding path loss, and the carrier frequencies. SVR presents the accuracy estimation of simulated datasets computing which shows Mean Square Error (MSE) as an average value of estimation errors that are squared, Root Mean Square Error (RMSE) as another parameter for measuring the accuracy of a estimation as a root value of MSE Average Root also R as the coefficient of determination tool for measuring the ability of a model in explaining dependent variable variations. If the value of R approaches one, it means that predictive results can follow variable patterns or variations well dependent. Cross Validation is a performance measurement. The aim is to find the best hyper-parameter combination so that machine learning can predict data accurately and prevent over-fitting problems. Optimal parameter values are determined by using the Grid Search Method, where machine learning will do modeling using the range C and ϵ given. Therefore, SVR Hyper-Parameter shows the most optimized parameter with C which affects the penalty given when there is an error in classification, Gamma that affects the pace of learning process, Epsilon indicates the error limit than can be ignored. The parameter values that produce the highest accuracy or the smallest error will be chosen as the best parameter.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]5G-R,Coefficient of determination,Path loss,Performance measurements,Railway communications,Received power,Root mean square errors,Support vector regression (SVR)[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]5G-R,Estimation Machine Learning,Path Loss,Received Power Signal,SVR[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]ACKNOWLEDGMENT This work is supported by the National Key Research and Development Program under Grant 2016YFE0200900, China. Corresponding author: Bo Ai (e-mail: boai@ieee.org). It is also supported by USAID through Sustainable Higher Education Research Alliances (SHERA) Program – Centre for Collaborative (CCR) National Center for Sustainable Transportation Technology (NCSTT).[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICEVT48285.2019.8994017[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]