MAC Protocol Design Optimization Using Deep Learning
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.
2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)