  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.
