MAC Protocol Design Optimization Using Deep Learning
This work addresses the challenge of interpretability in ML-based protocol design for networking researchers, though it is incremental as it builds on existing DRL methods.
The authors tackled the problem of designing and evaluating networking protocols by proposing a DRL-based framework that decouples protocols into parametric modules for better interpretability, and they demonstrated this with DeepMAC, which adapts to different 802.11 WLAN scenarios.
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.