Centralized versus decentralized multi-cell resource and power allocation for multiuser OFDMA networks

Yassin, Mohamad; Lahoud, Samer; Khawam, Kinda; Ibrahim, Marc; Mezher, Dany; Cousin, Bernard

doi:10.1016/j.comcom.2017.04.002

Cited by 10 publications

(5 citation statements)

References 32 publications

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“…We, however, consider these requirements in the design of the proposed scheme. A decentralized resource and power allocation algorithm is proposed for a multiuser OFDMA network in [20] and compared with centralized approaches.…”

Section: Related Workmentioning

confidence: 99%

Decentralized Resource Allocation-Based Multiagent Deep Learning in Vehicular Network

Mafuta

Maharaj

Alfa

2023

IEEE Systems Journal

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Resource allocation (RA) has a significant impact on vehicular network performance. With high mobility, RA is more challenging, as the number of vehicles in close proximity changes dynamically in the nonstationary environment. In this article, we propose a multiagent double deep Q-networks scheme to stabilize the system and maximize the sum-capacity of the vehicle-toinfrastructure (V2I) links, while satisfying the reliability and delay constraints for vehicle-to-vehicle (V2V) links. To avoid interference caused by unstable V2V links, a transmission mode selection is considered in the scheme design. In addition, we introduce a binarized weight algorithm to accelerate the deep neural network learning process and, therefore, improve the computational complexity of our scheme. Through extensive simulations and complexity analysis, we demonstrate that the proposed scheme yields excellent performance in terms of the sum-rate and probability rate of V2I and V2V communication modes. We also compare the proposed scheme with binarized weights with other algorithms in terms of accuracy evaluation.

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Section: Related Workmentioning

confidence: 99%

Decentralized Resource Allocation-Based Multiagent Deep Learning in Vehicular Network

Mafuta

Maharaj

Alfa

2023

IEEE Systems Journal

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“…In this section we consider welfare basis functions that are non-decreasing and convex, resulting in a monotone and supermodular total welfare W (a). Applications featuring this property include but are not limited to clustering and image segmentation [33], power allocation in multiuser networks [34]. In the following we explicitly characterize the price of anarchy for the class of supermodular resource allocation problems as a function of f (Theorem 3), extending [10], [11].…”

Section: The Case Of Supermodular Welfare Functionmentioning

confidence: 99%

Utility Design for Distributed Resource Allocation -- Part II: Applications to Submodular, Covering, and Supermodular Problems

Paccagnan¹,

Marden²

2018

Preprint

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A fundamental component of the game theoretic approach to distributed control is the design of local utility functions. In Part I of this work we showed how to systematically design local utilities so as to maximize the induced worst case performance. The purpose of the present manuscript is to specialize the general results obtained in Part I to a class of monotone submodular, supermodular and set covering problems. In the case of set covering problems, we show how any distributed algorithm capable of computing a Nash equilibrium inherits a performance certificate matching the well known 1 − 1/e approximation of [1]. Relative to the class of submodular maximization problems considered here, we show how the performance offered by the game theoretic approach improves on existing approximation algorithms. We briefly discuss the algorithmic complexity of computing (pure) Nash equilibria and show how our approach generalizes and subsumes previously fragmented results in the area of optimal utility design. Two applications and corresponding numerics are presented: the vehicle target assignment problem and a coverage problem arising in distributed caching for wireless networks.

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“…In this section we consider welfare basis functions that are non-decreasing and convex, resulting in a monotone and supermodular total welfare W (a). Applications featuring this property include clustering and image segmentation [SK10], power allocation in multiuser networks [Yas+17]. In the following we explicitly characterize the price of anarchy for the class of supermodular resource allocation problems as a function of f (Theorem 13), extending [JM18;PM17b].…”

Section: The Case Of Supermodular Welfare Functionmentioning

confidence: 99%

Distributed Control and Game Design: From Strategic Agents to Programmable Machines

Paccagnan¹

2018

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i sincere thanks goes to you for your support and collaboration during the early stages of my PhD. I remember vividly how much help and enthusiasm you put in teaching me how to write my "first article". If all of this is possible, it is in good part also thanks to you.The atmosphere we breath at IfA is absolutely incredible: a great mix of hardworking, positive energy, and friendship. Sometimes, bent over our papers, we seem to forget how lucky we are. Fortunately (or not), as soon as we step out of the ETH domain, we get reminded of what a privilege this is. I would like to express my greatest gratitude to all those people that made this place what it is. In this regard, there are many colleagues I should thank, but I limit myself to the essential. First of all, I would like to thank Basilio and Francesca for sharing with me a good half of their PhDs, and countless hours on the board conjecturing results that would be proved wrong only a few hours later. I wish both of you a brilliant career as you truly deserve. A very special thanks is due to Nicolò for sharing innumerable moments inside and outside of the office, for the many jokes we have been laughing of, and for the unmissable extra slice of cake (Lucia's). A big thanks goes to the Italian Gang -Basilio, Francesca, Nicolò, Giampaolo, Marcello, Saverio -for the countless adventures, and for making me feel as IfA was the extension of my time in high school. Thank you Tony for being such a positive, cheerful and selfless person. Thank you Peyman for the numerous research discussions we had: I feel there is a lot to learn form you. Thanks to the dream team -Juan, Marcello, Paul -for all the mountains adventures we shared: I will hardly forget when we set foot on the Dammazwillinge for the first time. I am confident you will not forget either. A special thanks goes to Paul for being a terrific office mate, and for sharing the first years of your PhD with me, often out in the Alps. At this point, I feel I owe a big thanks to our secretaries, Sabrina and Tanja, for making everything run incredibly smooth and for being extremely helpful with really anything. "If there is a solution, we will find it" Sabrina told me once, and this pretty much says it all.At a personal level, I would like to thank all my friends in Zurich and elsewhere, in particular Chiara for the gazillions hours we spent climbing together, Federica for being a terrific housemate, Martina and Alessandro for hosting me after my return from the US. A line of his own is needed (and probably not enough) to thank Alessio: a very special thought goes to you for all the things we went through together, way too many to recall. I will never forget the 20-hours day on the Allighesi ferrata, our first climbing route in the dolomites, or simply the usual giro di Ca' Tron. Amongst everything, I would like to thank you for teaching me to be ambitious, to fight, to never give up and to endure pain. Last, I want to thank you for always finding time to meet with me (the usual run) when I come back from Zurich, reg...

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Centralized versus decentralized multi-cell resource and power allocation for multiuser OFDMA networks

Cited by 10 publications

References 32 publications

Decentralized Resource Allocation-Based Multiagent Deep Learning in Vehicular Network

Decentralized Resource Allocation-Based Multiagent Deep Learning in Vehicular Network

Utility Design for Distributed Resource Allocation -- Part II: Applications to Submodular, Covering, and Supermodular Problems

Distributed Control and Game Design: From Strategic Agents to Programmable Machines

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