A New Method to Improve Frequent-Handover Problem in High-Mobility Communications Using RIC and Machine Learning

Abstract

The current trend of newer cellular network technology, such as 5G, is using a higher frequency spectrum that causes a smaller cell size. This will further cause a more frequent handover in the high-mobility users like the ones that happen in the high-speed train. In a cellular network, the handover process is very crucial as it may disrupt data transmission. Without a reliable handover process, the high-mobility users may experience problems like a high bit error rate (BER) or even call-drop. The traditional handover algorithm is proven reliable in ideal conditions but may not work correctly in a non-ideal condition such as the presence of a coverage hole. Machine learning can be implemented to improve the handover performance in those conditions. Open Radio Access Network (O-RAN) presents a solution to implement machine learning in the cellular network using a Radio Intelligent Controller (RIC), where we can improve a lot of functionalities in the Radio Access Network (RAN) modularly without modifying the existing RAN network element. The RIC original software is using vector autoregression to determine the target cell by predicting the throughput of each neighboring cell. In this paper, we performed two modifications to the original software: improve the vector autoregression method to consider the User Equipment (UE) movement and replace the vector autoregression method with a neural network. We also prove that these modifications present easier and better target cell determination for the environment with a coverage hole that will be useful for frequent handover in high-mobility users.

Keywords

• Cellular
• handover
• machine learning
• neural network
• O-RAN