Unsupervised Learning for C-RAN Power Control and Power Allocation

Nikbakht, Rasoul; Jönsson, Anders; Lozano, Angel

doi:10.1109/lcomm.2020.3027991

Cited by 14 publications

(15 citation statements)

References 16 publications

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“…The results match those produced by convex solvers with extreme accuracy. In the follow-up letters [11] and [12], more involved problems motivated by wireless communications are tackled and major advantages in computational scalability are revealed relative to convex solvers. For some of these problems, furthermore, the feasible set is convex but the objective function is not.…”

Section: Discussionmentioning

confidence: 99%

“…Before proceeding, in follow-up letters [11] and [12], to more specific parametric optimizations motivated by wireless communications, let us herein entertain a simpler yet much more general one.…”

Section: Application To Constrained Qpmentioning

confidence: 99%

“…By increasing it, the (very small) probability that the constraints are violated and that the solution thus falls outside the feasible set could be reduced further, at the expense of the objective value. This issue is tackled in [12] within the context of a wireless communications problem.…”

Section: A Formulationmentioning

confidence: 99%

“…For the QP herein invoked to illustrate the performance, however, the computational savings and the improvement in scalability are modest. It is for more intricate problems such as the ones tackled in [11], [12] that the advantage becomes prominent, hence its assessment is deferred to these sequels.…”

Section: Computational Costmentioning

confidence: 99%

“…In two follow-up letters, we turn to more involved problems motivated by wireless communications and not always exhibiting convex objectives. Precisely, power control for cellular networks is considered in [11] whereas power control for centralized radio-access networks is the object of [12].…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Learning for Parametric Optimization

2021

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This letter proposes the unsupervised training of a feedforward neural network to solve parametric optimization problems involving large numbers of parameters. Such unsupervised training, which consists in repeatedly sampling parameter values and performing stochastic gradient descent, foregoes the taxing precomputation of labeled training data that supervised learning necessitates. As an example of application, we put this technique to use on a rather general constrained quadratic program. Follow-up letters subsequently apply it to more specialized wireless communication problems, some of them nonconvex in nature. In all cases, the performance of the proposed procedure is very satisfactory and, in terms of computational cost, its scalability with the problem dimensionality is superior to that of convex solvers.

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Section: Discussionmentioning

confidence: 99%

Section: Application To Constrained Qpmentioning

confidence: 99%

Section: A Formulationmentioning

confidence: 99%

Section: Computational Costmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unsupervised Learning for Parametric Optimization

2021

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Resource allocation in 5G cloud‐RAN using deep reinforcement learning algorithms: A review

Khani,

Jamali,

Sohrabi

et al. 2023

Trans Emerging Tel Tech

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This paper reviews recent research on resource allocation in 5G cloud‐based radio access networks (C‐RAN) using deep reinforcement learning (DRL) algorithms. It explores the potential of DRL for learning complex decision‐making policies without human intervention. The paper first introduces the C‐RAN architecture and resource allocation concepts, followed by an overview of DRL algorithms applied to C‐RAN. It discusses the challenges and potential solutions in applying DRL to C‐RAN resource allocation, including scalability, convergence, and fairness. The review concludes by highlighting open research directions for future investigation. By providing insights into the state‐of‐the‐art techniques for resource allocation in 5G C‐RAN using DRL, this paper emphasizes their potential impact on advancing 5G network technology.

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What Will the Future of UAV Cellular Communications Be? A Flight From 5G to 6G

Geraci

García‐Rodríguez

Azari

et al. 2022

IEEE Commun. Surv. Tutorials

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224

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What will the future of UAV cellular communications be? In this tutorial article, we address such a compelling yet difficult question by embarking on a journey from 5G to 6G and expounding a large number of case studies supported by original results. We start by overviewing the status quo on UAV communications from an industrial standpoint, providing fresh updates from the 3GPP and detailing new 5G NR features in support of aerial devices. We then dissect the potential and the limitations of such features. In particular, we demonstrate how sub-6 GHz massive MIMO can successfully tackle cell selection and interference challenges, we showcase encouraging mmWave coverage evaluations in both urban and suburban/rural settings, and we examine the peculiarities of direct device-todevice communications in the sky. Moving on, we sneak a peek at next-generation UAV communications, listing some of the use cases envisioned for the 2030s. We identify the most promising 6G enablers for UAV communication, those expected to take the performance and reliability to the next level. For each of these disruptive new paradigms (non-terrestrial networks, cellfree architectures, artificial intelligence, reconfigurable intelligent surfaces, and THz communications), we gauge the prospective benefits for UAVs and discuss the main technological hurdles that stand in the way. All along, we distil our numerous findings into essential takeaways, and we identify key open problems worthy of further study.

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Unsupervised Learning for C-RAN Power Control and Power Allocation

Cited by 14 publications

References 16 publications

Unsupervised Learning for Parametric Optimization

Unsupervised Learning for Parametric Optimization

Resource allocation in 5G cloud‐RAN using deep reinforcement learning algorithms: A review

What Will the Future of UAV Cellular Communications Be? A Flight From 5G to 6G

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