TRUMP: Efficient and Flexible Realization of Medium Access Control Protocols for Wireless Networks

Zhang, Xi; Ansari, Junaid; Yang, Guangwei; Mähönen, Petri

doi:10.1109/tmc.2015.2499180

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…Their main drawback is that even for a moderate complicated system, they result in large diagrams. To overcome this, in the literature, prior works [55], [58], [70] use extended FSMs to model MAC protocols based on the block(modular)-based concept to add flexibility to protocol design, and reusability of the components. The existing (block-based) frameworks consider either a static way of connecting the components together or add a limited degree of dynamics in binding the components together.…”

Section: B Protocol Building Blocksmentioning

confidence: 99%

Towards a Learning-Based Framework for Self-Driving Design of Networking Protocols

Pasandi

Nadeem

2021

IEEE Access

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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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Section: B Protocol Building Blocksmentioning

confidence: 99%

Towards a Learning-Based Framework for Self-Driving Design of Networking Protocols

Pasandi

Nadeem

2021

IEEE Access

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show abstract

“…Their main drawback is that even for a moderate complicated system, they result in large diagrams. To overcome this, in the literature, prior works [69,73,91] use extended FSMs to model MAC protocols based on the block(modular)-based concept to add exibility to protocol design, and reusability of the components. The existing (block-based) frameworks consider either a static way of connecting the components together, or add a limited degree of dynamics in binding the components together.…”

Section: Protocol Building Blocksmentioning

confidence: 99%

Towards A Machine Learning-Based Framework For Automated Design of Networking Protocols

Pasandi

2019

2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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Networking protocols are designed through long-time and hard-work human eorts. Machine Learning (ML)-based solutions have been developed for communication protocol design to avoid manual eorts to tune individual protocol parameters. While other proposed ML-based methods mainly focus on tuning individual protocol parameters (e.g., adjusting contention window), our main contribution is to propose a novel Deep Reinforcement Learning (DRL)-based framework to systematically design and evaluate networking protocols. We decouple a protocol into a set of parametric modules, each representing a main protocol functionality that 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 Deep-MAC a framework in which a MAC protocol is decoupled into a set of blocks across popular avors of 802.11 WLANs (e.g., 802.11 b/a/g/n/ac). We are interested to see what blocks are selected by DeepMAC across dierent networking scenarios and whether DeepMAC is able to adapt to network dynamics.

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DMDL: A hierarchical approach to design, visualize, and implement MAC protocols

Wang

Petrova

Mähönen

2018

2018 IEEE Wireless Communications and Networking Conference (WCNC)

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TRUMP: Efficient and Flexible Realization of Medium Access Control Protocols for Wireless Networks

Cited by 4 publications

References 32 publications

Towards a Learning-Based Framework for Self-Driving Design of Networking Protocols

Towards a Learning-Based Framework for Self-Driving Design of Networking Protocols

Towards A Machine Learning-Based Framework For Automated Design of Networking Protocols

DMDL: A hierarchical approach to design, visualize, and implement MAC protocols

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