Authors: Vijay Bisen, Dr. N.K. Singh
Abstract: One of the key issues in ensuring uninterrupted service is the handover process — the transition of a mobile device's connection from one base station to another. Traditional handover mechanisms, while functional, often struggle with the dynamic and complex environments of modern networks. This has led to increasing interest in leveraging machine learning (ML) to optimize and automate the handover process, enhancing both performance and user experience. Orthogonal Frequency Division Multiplexing (OFDM) and Non-Orthogonal Multiple access (NOMA) have been the leading contenders for modern wireless networks. NOMA is a technique in which multiple users data is separated in the power domain. In the proposed approach , a machine learning based handover mechanism between OFDM and NOMA has been proposed based on channel conditions. The condition for switching or handover has been chosen as the BER of the system. A comparative analysis with existing work indicates that the proposed scheme outperforms the existing techniques in terms of SNR requirement thereby making the system more practically useful for fading channel conditions.
