论文标题
深钢筋学习辅助随机访问
Deep Reinforcement Learning-Aided Random Access
论文作者
论文摘要
我们考虑了一个系统模型，该模型由访问点（AP）和k互联网（IoT）节点组成，该节点偶尔会活跃以将数据发送到AP。假定AP具有N时间频资源块，它可以分配给希望发送数据的IoT节点，其中N <K。主要问题是如何在每个时间插槽中分配n时间频率的资源块为IoT节点，以使平均数据集率最大化。对于这个问题，我们提出了深入的增强学习（DRL）辅助随机访问（RA）方案，在此方案中，AP的智能DRL代理学会在每次插槽中预测IoT节点的活动，并授予对IOT节点的时间频率块，以预测为有效的IoT节点。接下来，由DRL代理商将无活性的物联网节点以及在单元格中未见或新到达的节点进行的，采用标准RA方案来获得时间频率资源块。我们利用专家知识来更快地培训DRL代理。与实践中使用的现有解决方案相比，我们的数值结果表明，实施提出的DRL ADAID RA方案（标准RA方案）时，平均数据包率有显着改善。
We consider a system model comprised of an access point (AP) and K Internet of Things (IoT) nodes that sporadically become active in order to send data to the AP. The AP is assumed to have N time-frequency resource blocks that it can allocate to the IoT nodes that wish to send data, where N < K. The main problem is how to allocate the N time-frequency resource blocks to the IoT nodes in each time slot such that the average packet rate is maximized. For this problem, we propose a deep reinforcement learning (DRL)-aided random access (RA) scheme, where an intelligent DRL agent at the AP learns to predict the activity of the IoT nodes in each time slot and grants time-frequency resource blocks to the IoT nodes predicted as active. Next, the IoT nodes that are missclassified as non-active by the DRL agent, as well as unseen or newly arrived nodes in the cell, employ the standard RA scheme in order to obtain time-frequency resource blocks. We leverage expert knowledge for faster training of the DRL agent. Our numerical results show significant improvements in terms of average packet rate when the proposed DRL-aided RA scheme is implemented compared to the existing solution used in practice, the standard RA scheme.
