Comparison of Theoretical and Simulated Call Blocking Rates

In communication system performance evaluation, call blocking rate is a key metric that reflects the probability of the system rejecting new call requests when resources are constrained. Theoretical analysis and simulation modeling are two commonly used research methods, each with distinct advantages and applicable scenarios.

Theoretical analysis typically employs mathematical models such as queuing theory or probability models, which can quickly derive analytical expressions for blocking rates. This method applies to simplified scenarios and facilitates understanding of fundamental system behavior patterns. However, theoretical models often require idealized assumptions about practical systems, which may lead to deviations from actual results.

Simulation modeling constructs computer models of the system to simulate real call processes and statistically calculate blocking rates. This approach can more flexibly incorporate complex factors (such as dynamic loads, non-uniform call distributions), with results typically closer to real-world situations. Code implementations might use discrete-event simulation frameworks with key functions handling call arrival generation (Poisson processes), resource allocation algorithms, and blocking event counters. However, simulations require significant computational resources, and parameter settings substantially impact results.

When comparing results from both methods, discrepancies between theoretical and simulated values may require checking model assumption validity or simulation parameter accuracy. Ideally, both should demonstrate convergence trends, which can validate theoretical correctness while confirming simulation reliability. This comparative analysis provides crucial foundations for communication system design and optimization. Common implementation approaches include plotting both results on the same graph, calculating statistical differences, and performing sensitivity analysis on key parameters.