MAC Protocol Design Optimization Using Deep Learning

Pasandi, Hannaneh Barahouei; Nadeem, Tamer

doi:10.1109/icaiic48513.2020.9065254

Cited by 14 publications

(7 citation statements)

References 23 publications

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“…However, contrary to our endeavor to develop novel semantic communication protocols, most works on emergent communication protocol design are based on learning an NN for a given environment such as wireless MAC using MADRL [7], sensor networks, and low-earth orbit satellite networks [21], [22]. In addition, in [23], the authors apply NN for optimizing the MAC pipeline by selecting appropriate protocols. However, the performance is bounded by classical protocols that lack grounding in the actual environment.…”

Section: A Related Workmentioning

confidence: 99%

Towards Semantic Communication Protocols: A Probabilistic Logic Perspective

Seo¹,

Park²,

Ko³

et al. 2022

Preprint

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Classical medium access control (MAC) protocols are interpretable, yet their task-agnostic control signaling messages (CMs) are ill-suited for emerging mission-critical applications. By contrast, neural network (NN) based protocol models (NPMs) learn to generate task-specific CMs, but their rationale and impact lack interpretability. To fill this void, in this article we propose, for the first time, a semantic protocol model (SPM) constructed by transforming an NPM into an interpretable symbolic graph written in the probabilistic logic programming language (ProbLog). This transformation is viable by extracting and merging common CMs and their connections while treating the NPM as a CM generator. By extensive simulations, we corroborate that the SPM tightly approximates its original NPM while occupying only 0.02% memory. By leveraging its interpretability and memory-efficiency, we demonstrate several SPM-enabled applications such as SPM reconfiguration for collision-avoidance, as well as comparing different SPMs via semantic entropy calculation and storing multiple SPMs to cope with non-stationary environments.

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Section: A Related Workmentioning

confidence: 99%

Towards Semantic Communication Protocols: A Probabilistic Logic Perspective

Seo¹,

Park²,

Ko³

et al. 2022

Preprint

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“…Within this body of research, contention-based policies dominate (see [2], [3], [4] or [5]), although work on arXiv:2007.09948v1 [cs.IT] 20 Jul 2020 coordinated protocols also exists (e.g., [6], [7], [8]). Other approaches such as [9] propose learning to enable/disable existing MAC features.…”

Section: A Related Workmentioning

confidence: 99%

Toward Joint Learning of Optimal MAC Signaling and Wireless Channel Access

Valcarce

Hoydis

2021

IEEE Trans. Cogn. Commun. Netw.

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Communication protocols are the languages used by network nodes to accomplish their tasks. Before a User Equipment (UE) can exchange a data bit with the Base Station (BS), it must first negotiate the conditions and parameters for that transmission, which is supported by signaling messages at all layers of the protocol stack. Each year, the mobile communications industry invests large sums of money to define and standardize these messages, which are designed by humans during lengthy technical (and often political) debates. But is this the only way to develop a protocol? Could machines emerge their own signaling protocols automatically and without human intervention? We address the question of whether radios can learn a pre-given target language (i.e., a signaling) as an intermediate step towards evolving their own. Furthermore, we train cellular radios to emerge a channel access policy that performs optimally under the constraints of the target signaling. We show that multi-agent reinforcement learning (MARL) and learning-to-communicate techniques achieve this goal with gains over expert systems. Finally, we provide insight into the transferability of these results to scenarios never seen during training.

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“…In our previous works [47], [48] we targeted IEEE 802.11 MAC protocol design. MAC protocol was decoupled into its set of building blocks and then the agent selected the appropriate protocol blocks based on varying network conditions.…”

Section: Related Workmentioning

confidence: 99%

“…MAC protocol was decoupled into its set of building blocks and then the agent selected the appropriate protocol blocks based on varying network conditions. In this article, we build and expand on our earlier works [47], [48] in different aspects. Mainly, in this article, we propose a general Reinforcement Learning (RL)-based framework that could be adapted to optimize the design of not only MAC protocols but of communication protocols across all network stack.…”

Section: Related Workmentioning

confidence: 99%

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

Pasandi

Nadeem

2021

IEEE Access

Self Cite

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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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MAC Protocol Design Optimization Using Deep Learning

Cited by 14 publications

References 23 publications

Towards Semantic Communication Protocols: A Probabilistic Logic Perspective

Towards Semantic Communication Protocols: A Probabilistic Logic Perspective

Toward Joint Learning of Optimal MAC Signaling and Wireless Channel Access

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

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