作者
Marvin Manalastas, Muhammad Umar Bin Farooq, Syed Muhammad Asad Zaidi, Aneeqa Ijaz, Waseem Raza, Ali Imran
发表日期
2023/1/8
研讨会论文
2023 IEEE 20th Consumer Communications & Networking Conference (CCNC)
页码范围
684-685
出版商
IEEE
简介
This paper presents and evaluates a simple but effective approach for substantially reducing inter-frequency handover (HO) failure rate. We build a machine learning model to forecast inter-frequency HO failures. For improved accuracy compared to the state-of-the-art models, we use domain knowledge to identify and leverage the model input features. These features include reference signal received power (RSRP) of the source and target base stations as well as the RSRP of the interferers for both the source and the target layers. Six machine learning classifiers are tested with the highest accuracy of 93% observed for the XGBoost classifier. The novel idea to include the RSRP of the interferes improved the accuracy of XGBoost by 10%.
学术搜索中的文章
Machine learning-based handover failure prediction model for handover success rate improvement in 5g
M Manalastas, MUB Farooq, SMA Zaidi, A Ijaz, W Raza… - 2023 IEEE 20th Consumer Communications & …, 2023