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.

翻译：在无线网络的接收控制方面,关键挑战是在阻断新请求的可能性之间作出最佳权衡,同时尽量减少不断请求的减少的可能性。我们考虑了解决接收控制问题的两种办法:(一) 通常采用的门槛政策和(二) 我们提出的依靠神经网络强化学习的政策。进行了广泛的模拟实验,分析这两种政策的执行情况。结果显示,强化学习政策在假设情况中优于门槛政策,不同时间的到达率和多种用户设备类型,证明它适用于现实的无线网络情景。