  The key challenge in admission control in wireless networks is to strike an
optimal trade-off between the blocking probability for new requests while
minimizing the dropping probability of ongoing requests. We consider two
approaches for solving the admission control problem: i) the typically adopted
threshold policy and ii) our proposed policy relying on reinforcement learning
with neural networks. Extensive simulation experiments are conducted to analyze
the performance of both policies. The results show that the reinforcement
learning policy outperforms the threshold-based policies in the scenario with
heterogeneous time-varying arrival rates and multiple user equipment types,
proving its applicability in realistic wireless network scenarios.
