Maximizing user retention with machine learning enabled 6G channel allocation

The paper proposes a machine learning-based user retention technique for the 6G network by identifying and classifying loyal users using supervised machine learning algorithms such as Decision Tree, K-Nearest Neighbor, and Support Vector Machine. The study also suggests a threshold-based channel allocation method to allocate network resources primarily to loyal users. The performance of the proposed algorithm is evaluated using SimPy simulation, and the results show that loyal users experience minimum average waiting time and no call drops compared to normal and recent users. The paper highlights the need for new schemes and policies to retain valuable users and proposes a novel approach that leverages machine learning techniques and 6G aspects to achieve this objective. The proposed algorithm's effectiveness is demonstrated through simulation, which provides useful insights into its performance under different network conditions. The research contributes to the ongoing efforts to enhance user experience in the rapidly evolving field of mobile communication networks.