Reinforcement learning based handover mechanism for next generation mobile communication systems

Next-generation mobile communication networks have been established on critical enabling technologies such as millimeter-wave usage, cloud-native architectures, and new intelligent algorithms to meet the increasing demands of new services and requirements. One important research area for the new generation of networks is Radio Resource Management (RRM) applications. In this thesis, a reinforcement learning-based handover (HO) mechanism is designed by the concept of Contextual Multi-Armed Bandit (CMAB) algorithm named CHARM (CMAB-Based Handover Algorithm in Reinforcement Mechanism) and considering Open-Radio Access Network (O-RAN) architecture. The speed of user equipment (UE) and Signal-to-Interference-plus-Noise Ratio (SINR) of the serving Base Station (BS) parameters are evaluated as the context information for the algorithm. The proposed algorithm is compared with the traditional algorithm of 3rd Generation Partnership Project (3GPP) and a rival reinforcement algorithm in the literature under different channel conditions such as Urban Macro (UMa), Urban Micro (UMi) propagation, and different intensities of BS and obstacles on the map. The results show that our algorithm outperforms the traditional 3GPP HO algorithm and the rival algorithm for average information rate under every channel condition. According to the simulations, it is also highly competitive for average HO numbers.