MAC Protocol Design Optimization Using Deep Learning

• URL: http://arxiv.org/abs/2002.02075v1
• Date: Thu, 6 Feb 2020 02:36:52 GMT
• Title: MAC Protocol Design Optimization Using Deep Learning
• Authors: Hannaneh Barahouei Pasandi, Tamer Nadeem
• Abstract summary: 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, our main contribution is to decouple a protocol into a set of parametric modules. 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 802.11s.
• Score: 1.5469452301122173
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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