MAC Protocol Design Optimization Using Deep Learning
MAC Protocol Design Optimization Using Deep Learning
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DOI：
10.1109/icaiic48513.2020.9065254
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
H. Pasandi;T. Nadeem
中科院分区：
文献类型：
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作者：
H. Pasandi;T. Nadeem
基于深度学习（DL）的解决方案最近已被开发用于通信协议设计。这种基于学习的解决方案可以避免手动调整各个协议参数。虽然这些解决方案看起来很有前途，但由于ML技术的黑盒性质，它们很难解释。为此，我们提出了一种新的DRL为基础的框架，系统地设计和评估网络协议。而其他提出的基于ML的方法主要集中在调整各个协议参数（例如，调整竞争窗口），我们的主要贡献是将协议解耦为一组参数模块，每个模块代表一个主要的协议功能，并用作DRL输入，以更好地理解生成的协议设计优化，并以系统的方式分析它们。作为一个案例研究，我们介绍和评估DeepMAC一个框架，在这个框架中，MAC协议被解耦成一组跨流行风格的802.11 WLAN的块（例如，802.11a/b/g/n/ac).我们有兴趣看看DeepMAC在不同的网络场景中选择了哪些块，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.