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Networking protocols are designed through long-standing and hard-working human efforts. Machine Learning (ML)-based solutions for communication protocol design have been developed to avoid manual effort to adjust individual protocol parameters. While other proposed ML-based methods focus mainly on tuning individual protocol parameters (e.g. contention window adjustment), our main contribution is to propose a new Deep Reinforcement Learning (DRL) framework to systematically design and evaluate networking protocols. We decouple the protocol into a set of parametric modules, each representing the main protocol functionality that is used as a DRL input to better understand and systematically analyze the optimization of generated protocols. As a case study, we introduce and evaluate DeepMAC a framework in which the MAC protocol is decoupled into a set of blocks across popular 802.11 WLANs (e.g. 802.11 a/b/g/n/ac). We are interested to see which blocks are selected by DeepMAC across different networking scenarios and whether DeepMAC is capable of adapting to network dynamics.INDEX TERMS Communication protocols, deep learning, machine-generated algorithm, protocol design, reinforcement learning.This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2021.3061729, IEEE Access H. B. Pasandi, T. Nadeem: Towards a Learning-Based Framework for Self-Driving Design of Networking Protocols II. BACKGROUND HANNANEH BARAHOUEI PASANDI (Student Member, IEEE) received the master's degree from Université Grenoble Alpes. She is currently pursuing the Ph.D. degree with the Department of Computer Science, Virginia Commonwealth University. Her research interests include wireless communication, with an emphasis on the design and implementation of data-driven MAC protocols that not only consider conventional factors but also extract and include important environmental factors in designing protocols for IEEE WLANs.

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Evolving amendments of 802.11 standards feature a large set of physical and MAC layer control parameters to support the increasing communication objectives spanning application requirements and network dynamics. The significant growth and penetration of various devices come along with a tremendous increase in the number of applications supporting various domains and services which will impose a never-before-seen burden on wireless networks. The challenge however, is that each scenario requires a different wireless protocol functionality and parameter setting to optimally determine how to tune these functionalities and parameters to adapt to varying network scenarios. The traditional trialerror approach of manual tuning of parameters is not just becoming difficult to repeat but also sub-optimal for different networking scenarios. In this paper, we describe how we can leverage a deep reinforcement learning framework to be trained to learn the relation between different parameters in the physical and MAC layer and show that how our learning-based approach could help us in getting insights about protocol design optimization task.

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