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Modeling the probability of blocking User Equipment (UE) sessions within a cell is a crucial aspect within the management of 5G services with Guaranteed Bit Rate (GBR) requirements, especially in the process of planning in advance the deployment of such services. The complexity of modeling the UE blocking probability arises from the dependency of this performance indicator on multiple factors, including the UE channel quality within the cell, the MAC scheduling discipline, the statistical distributions of the traffic demand and session duration, and the GBR requirements of the corresponding service. In this vein, we propose an analytical model to assess the UE blocking probability for a GBR service. The proposed model is based on a Markov chain which is insensitive to the holding time distribution of the UE data sessions. Furthermore, it may consider as input any continuous distribution for the average Signal-to-Interference-plus-Noise Ratio (SINR) within the cell. The simulation results demonstrate the execution time of the proposed model is on the order of tens of milliseconds, which makes it suitable for testing multiple network configurations in a short term, training ML models or detecting traffic anomalies in real time. Additionally, the results shows that our model exhibits an estimation error for the UE blocking probability below 2.6%.

Computationally Efficient UE Blocking Probability Model for GBR Services in Beyond 5G RAN