Learning Decentralized Wireless Resource Allocations with Graph Neural Networks

Wang, Zhiyang; Eisen, Mark; Ribeiro, Alejandro

doi:10.48550/arxiv.2107.01489

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Specifically, ML was a main player in the 5G and beyond communication systems. It has been adopted to provide modern communication systems with a plethora of intelligent services and functions such as intelligent resource allocation [7], [8], link adaptation [9], [10], beamforming [11],…”

Section: Introductionmentioning

confidence: 99%

Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey

Hussien¹

2023

Preprint

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<p>The last decade, (2012 - 2022), saw an unprecedented advance in machine learning (ML) techniques, particularly deep learning (DL). As a result of the proven capabilities of DL, a large amount of work has been presented and studied in almost every field. Since 2012, when the convolution neural networks have been reintroduced in the context of \textit{ImagNet} competition, DL continued to achieve superior performance in many challenging tasks and problems. Wireless communications, in general, and medium access control (MAC) techniques, in particular, were among the fields that were heavily affected by this improvement. MAC protocols play a critical role in defining the performance of wireless communication systems. At the same time, the community lacks a comprehensive survey that collects, analyses, and categorizes the recent work in ML-inspired MAC techniques. In this work, we fill this gap by surveying a long line of work in this era. We solidify the impact of machine learning on wireless MAC protocols. We provide a comprehensive background to the widely adopted MAC techniques, their design issues, and their taxonomy, in connection with the famous application domains. Furthermore, we provide an overview of the ML techniques that have been considered in this context. Finally, we augment our work by proposing some promising future research directions and open research questions that are worth further investigation.</p>

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Section: Introductionmentioning

confidence: 99%

Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey

Hussien¹

2023

Preprint

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“…Another approach, however, consists in training a DNN whose cost function is a weighted sum rate (WSR) of the users in the network, in an unsupervised manner [6,7,9]. Moreover, the model-free and distributed extensions are also introduced in the literature [10,11,12,13,14,15].…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Resource Allocation with Dynamic Data Rate Constraints

Behmandpoor

Patrinos

Moonen

2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we address the problem of resource allocation (RA) in wireless communication networks, where each user has a dynamic data rate constraint. The objective of RA is to maximize the sum rate (SR) of the users while satisfying the data rate constraints in expectation. For a given set of data rate constraints, a suitable probability distribution for the activation of users is found iteratively with a stochastic gradient descent (SGD) approach to satisfy the data rate constraints in expectation. At each time instant, RA amongst the randomly activated users is performed noniteratively by a centralized deep neural network (DNN). Simulations show that the proposed approach is convergent and not only can consider dynamic data rate constraints accurately, but also that it achieves a SR higher than that of the conventional geometric programming (GP) method. The proposed approach can open up a direction of research for cross-layer RA in the current deep learning-based RA context.

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Learning Decentralized Wireless Resource Allocations with Graph Neural Networks

Cited by 2 publications

References 22 publications

Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey

Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey

Learning-Based Resource Allocation with Dynamic Data Rate Constraints

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