Proposal and evaluation of classification methodologies for improving the handover process in cellular networks
The advent of Fifth generation (5G) and B5G future mobile networks has generated significant growth in the demand for reliable and low-latency data networks to support a wide range of applications and services with demanding requirements, such as monitoring, remote surgery, and vehicles connectivity...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/392858 |
| Acceso en línea: | https://hdl.handle.net/2117/392858 |
| Access Level: | acceso abierto |
| Palabra clave: | Global system for mobile communications Predicció Trajectòries Sistemes cel·lulars Xarxes neuronals Sistema global per a comunicacions mòbils Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Comunicacions mòbils |
| Sumario: | The advent of Fifth generation (5G) and B5G future mobile networks has generated significant growth in the demand for reliable and low-latency data networks to support a wide range of applications and services with demanding requirements, such as monitoring, remote surgery, and vehicles connectivity. To address these challenges, small cells have emerged as a solution, offering high data rates in dense device environments. However, the increased density of deployed cells leads to frequent handovers and signaling overheads, creating challenges for managing radio resources. This work explores the use of prediction algorithms to address these challenges by accurately predicting handovers to prepare in advance the required resources in the next cells visited by the User Equipment (UE). Various methodologies, including machine learning (ML) algorithms, have been proposed for predicting UE mobility and target base stations. Prediction models based on Markov models, Bayesian Networks, Neural Networks, Support Vector Machines (SVM), and clustering techniques have been developed to utilize mobility patterns and improve prediction accuracy. The main objective of the thesis is to develop a prediction model that utilizes historical user trajectory data to predict the next target base station during a handover process. The proposed methodology utilizes pre-processed subtrajectories as features to identify trajectory patterns and make predictions. Simulated mobility trajectories of vehicles in the city of Cologne are used to test the proposed approach. The model's performance is evaluated based on prediction certainty, anticipation time, and processing time. The performance of different classifiers and subtrajectory sizes is evaluated to determine the most effective model parameters. The Logistic Regression algorithm is found to provide the highest prediction certainty, while subtrajectories of size 2 yield faster processing times. The developed ML classification model is compared with a prediction methodology based on a Clustering model, showing that the ML classification model provides slightly worse prediction certainty but longer anticipation times and better computational efficiency. This work also illustrates potential scenarios where each of these two methodologies can be more suitable. In conclusion, the proposed ML classification methodology successfully develops a prediction model that utilizes historical trajectory data to predict target base stations for handovers. The ML classification model offers accurate predictions with reasonable anticipation time and computational efficiency. The comparative evaluation with the Clustering model highlights the strengths and weaknesses of each approach, making them suitable for different prediction scenarios based on cell coverage and trajectory characteristics. |
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