Modeling 5G-NR User Blocking Probability in Conditional Handover-Enabled Heterogeneous Networks

Conditional Handover (CHO), introduced in the 16th edition of the 3GPP standard, serves as an alternative to Baseline Handover (BHO). CHO aims to mitigate radio link failures (RLFs) during handover preparation by incorporating a dedicated base station (BS) reservation phase. While BS reservation effectively reduces RLF rates, it concurrently occupies valuable radio resources, potentially leading to user blockages in congested scenarios. In this research, we develop a mathematical model to quantify user blocking probability by employing Markov models and connecting this probability to handover management parameters using stochastic geometry to characterize user mobility and BS coverage. This formulation establishes a probabilistic link between handover management parameters and user blocking occurrences. It further reveals that optimization solely focused on minimizing user blocking can paradoxically increase RLF rates, highlighting the need to consider trade-offs among performance metrics. To demonstrate the practical applicability of the proposed model, we present a case study involving a multi-objective optimization problem solved using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This example highlights how the model can be used to balance key performance trade-offs, including user blocking probability, RLF probability, latency, and network data rate. The presented model can be utilized independently or integrated with a variety of optimization techniques according to the needs of service providers. These configurations may include the distribution intensity and biasing factor of BSs at each tier during the network establishment phase, as well as the optimal handover initiation parameters during network operation. Operators can use the model to guide network planning and tuning for improved stability, seamless connectivity, and enhanced user experience.