The existing medium access control (MAC) protocol of Wi-Fi networks (i.e., CSMA/CA) suffers from poor performance in large networks due to its incapability of handling transmission collisions. This drawback dramatically reduces the spectrum efficiency of Wi-Fi networks. To cope with this issue, we investigate a deep-learning (DL) based intelligent wireless MAC protocol, referred to as DL-MAC, to improve the spectrum efficiency of Wi-Fi networks. The goal of DL-MAC is to enable not only intelligent channel access, but also intelligent rate adaption to increase the throughput. Notably, our DL-MAC protocol is designed for the 2.4GHz frequency band and exploits the real wireless data sampled from actual environments that consist of many working devices. We design a deep neural network (DNN) that is trained using the sampled real data after data processing and exploit the trained DNN to implement our DL-MAC. The experimental results demonstrate that the DL-MAC protocol can achieve high throughput than CSMA/CA channel access and traditional rate adaptions.

翻译：现有Wi-Fi网络(即CSMA/CA)的中位访问控制协议(MAC)由于无法处理传输碰撞,在大型网络中表现不佳,这一缺陷大大降低了Wi-Fi网络的频谱效率。为了解决这一问题,我们调查了基于深层次学习(DL)的智能无线协议(称为DL-MAC),以提高Wi-Fi网络的频谱效率。DL-MAC的目标是不仅允许智能通道访问,而且允许智能速度调整,以增加吞吐量。值得注意的是,我们的DL-MAC协议是为2.4GHz频率波段设计的,并利用从由许多工作装置组成的实际环境中取样的真正无线数据。我们设计了一个深层的神经网络(DNNN),在数据处理后,利用经过培训的DNNN(DN)来实施我们的DL-MAC网络。实验结果显示,DL-MAC协议可以实现比CSMA/CA频道访问和传统速率调整的高通过量。