论文标题
Mac协议设计优化使用深度学习
MAC Protocol Design Optimization Using Deep Learning
论文作者
论文摘要
最近已经为通信协议设计开发了基于深度学习（DL）的解决方案。这种基于学习的解决方案可以避免手动努力调整单个协议参数。尽管这些解决方案看起来很有希望，但由于ML技术的黑盒性质，它们很难解释。为此，我们提出了一个基于DRL的新型框架来系统地设计和评估网络协议。尽管其他提出的基于ML的方法主要集中于调整单个协议参数（例如，调整争议窗口），但我们的主要贡献是将协议将协议解散到一组参数模块中，每种方案代表主要协议功能，并用作DRL输入来更好地了解生成的协议设计并在系统的时尚中分析它们。作为一个案例研究，我们介绍和评估DeepMac一个框架，其中MAC协议将其分解为802.11 WLAN的流行口味的一组块（例如802.11a/b/g/g/n/ac）。我们有兴趣了解DeepMac在不同的网络方案中选择了哪些块，以及DeepMac是否能够适应网络动态。
Deep learning (DL)-based solutions have recently been developed for communication protocol design. Such learning-based solutions can avoid manual efforts to tune individual protocol parameters. While these solutions look promising, they are hard to interpret due to the black-box nature of the ML techniques. To this end, we propose a novel DRL-based framework to systematically design and evaluate networking protocols. While other proposed ML-based methods mainly focus on tuning individual protocol parameters (e.g., adjusting contention window), our main contribution is to decouple a protocol into a set of parametric modules, each representing a main protocol functionality and is used as DRL input to better understand the generated protocols design optimization and analyze them in a systematic fashion. As a case study, we introduce and evaluate DeepMAC a framework in which a MAC protocol is decoupled into a set of blocks across popular flavors of 802.11 WLANs (e.g., 802.11a/b/g/n/ac). We are interested to see what blocks are selected by DeepMAC across different networking scenarios and whether DeepMAC is able to adapt to network dynamics.
