A Hybrid machine learning based model for congestion prediction in mobile networks

Brief Survey: Machine Learning in Handover Cellular Network

“…In supervised learning, a labeled analysis variable is essential, such as distinguishing between soft HOs (ensuring no interruption during the process) and hard HOs (characterized by an actual interruption of the connection during the transition between base stations) [7]. Unsupervised learning, in contrast, clusters data without relying on labels, as seen in the co-clustering algorithm based on the Latent Block Model (LBM) for grouping similar Long-Term Evolution (LTE) cells according to Key Performance Indicators (KPIs) for congestion prediction [8]. Reinforcement learning is also applicable, where rewards and penalties guide decision-making based on input data.…”