TY - GEN
T1 - Handover Analysis of Mobile Cellular Network in a Populated City
AU - Lupera-Morillo, Pablo
AU - Aguagallo, Patricio
AU - Villarreal, Jonathan
AU - Cando, Jhon
AU - Párraga, Viviana
AU - Grijalva, Felipe
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Mobile network operators continuously monitor QoS in terms of multiple metrics to ensure high network performance. Handover (HO) is one of the key issues in cellular communication networks, as it poses multiple threats to QoS. In this work, several real measured radio frequency (RF) parameters are used to analyze handover performance using statistics and Machine Learning. The measurements were collected through Android applications in 4G mobile networks in an urban area of the city of Quito, Ecuador where the existence of nearby base stations was checked. Variables were created for the analysis, such as one indicating the existence of a handover and another indicating whether the handover was successful or unsuccessful. The results of the statistical analysis show that performance metrics determine HO problems. While Machine Learning was used to classify whether the handover was successful or unsuccessful based on certain input parameters using Decision Trees. A predictive analysis was also made using Linear Regression to determine whether handover existed or not. Finally, the machine learning models were evaluated, obtaining an accuracy of 0.73 for the decision tree and an RSME of 0.077 for the regression.
AB - Mobile network operators continuously monitor QoS in terms of multiple metrics to ensure high network performance. Handover (HO) is one of the key issues in cellular communication networks, as it poses multiple threats to QoS. In this work, several real measured radio frequency (RF) parameters are used to analyze handover performance using statistics and Machine Learning. The measurements were collected through Android applications in 4G mobile networks in an urban area of the city of Quito, Ecuador where the existence of nearby base stations was checked. Variables were created for the analysis, such as one indicating the existence of a handover and another indicating whether the handover was successful or unsuccessful. The results of the statistical analysis show that performance metrics determine HO problems. While Machine Learning was used to classify whether the handover was successful or unsuccessful based on certain input parameters using Decision Trees. A predictive analysis was also made using Linear Regression to determine whether handover existed or not. Finally, the machine learning models were evaluated, obtaining an accuracy of 0.73 for the decision tree and an RSME of 0.077 for the regression.
KW - RSRQ
KW - handover
KW - handover analysis
KW - handover failure
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85187799920&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-54256-5_10
DO - 10.1007/978-3-031-54256-5_10
M3 - Contribución a la conferencia
AN - SCOPUS:85187799920
SN - 9783031542558
T3 - Lecture Notes in Networks and Systems
SP - 108
EP - 118
BT - Information Technology and Systems - ICITS 2024
A2 - Rocha, Alvaro
A2 - Diez, Jorge Hochstetter
A2 - Ferras, Carlos
A2 - Rebolledo, Mauricio Dieguez
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Information Technology and Systems, ICITS 2024
Y2 - 24 January 2024 through 26 January 2024
ER -