In mobile cellular design, one important quality-of-service metric is
the blocking probability. Using computer simulation for studying
blocking probability is quite time-consuming. Furthermore, existing
teletraffic models such as the Information Exchange Surrogate
Approximation (IESA) only give a rough estimate of blocking probability.
Another common approach, direct blocking probability evaluation using
neural networks (NN), performs poorly when extrapolating to network
conditions outside of the training set. This paper addresses the
shortcomings of existing teletraffic and NN-based approaches by
introducing a hybrid approach, namely IESA-NN. In IESA-NN, an
NN is used to estimate a tuning parameter, which is in turn used to
estimate the blocking probability via a modified IESA approach. In other
words, the teletraffic approach IESA still forms the core of IESA-NN,
with NN techniques used to improve the accuracy of the approach via the
tuning parameter. Simulation results show that IESA-NN performs better
than previous approaches based on NN or teletraffic theory alone. In
particular, even when the NN cannot produce a good value for the tuning
parameter, for example when extrapolating to network conditions not
experienced in the training set, the final IESA-NN estimate is generally
still accurate due to bounds set by the underlying teletraffic theory.