Machine learning improves mobile signal handover decisions in tough conditions
User-Level Handover Decision Making Based on Machine Learning Approaches
Machine LearningNetworking and Internet Architecture
Summary
Mobile devices often need to switch between different signal sources to maintain a good connection. This switching, called handover, can be tricky in places with obstacles like buildings or weak coverage. The authors compared different machine learning methods to help phones decide when and where to switch signals. They found some techniques that balance accuracy and speed well, even when the signal is poor. Their findings can make connections faster and more reliable.
What this means in practice
- •For mobile network operators: Improve network handover algorithms to reduce dropped calls and speed up data transfer in urban and blocked areas.
- •For cellular device developers: Incorporate efficient machine learning models to better predict when devices should switch between network cells under challenging signal conditions.
Authors
João Lima, Alvaro Medeiros, Eduardo Aguiar, Vicente Angelo de Sousa Junior, Tarciana Guerra
Abstract
This letter covers a broad comparison of methods for classification and regression applications for a user-level handover decision making in scenarios with adverse propagation conditions involving buildings, coverage holes, and shadowing effects. The simulation campaigns are based on network simulator ns-3. The comparison encompasses classical machine learning approaches, such as KNN, SVM, and neural networks, but also state-of-the-art fuzzy logic systems and latter boosting machines. The results indicate that SVM and MLP are the most suitable for the classification of the best handover target, although fuzzy system SOFL can perform similarly with lower processing time. Additionally, for the download time estimation, LightGBM provides the smallest error with short processing time, even in hard propagation scenarios.