Optimized Intelligent Design for Smart Systems Hybrid Beamforming and Power Adaptation Algorithms for Sensor Networks Decision-Making Approach

dc.authorid Tureli, Ufuk/0000-0002-4986-5270
dc.authorid Tureli, Didem/0000-0001-6835-2940
dc.authorscopusid 57214119178
dc.authorscopusid 35612536500
dc.authorscopusid 6602863624
dc.authorwosid Tureli, Ufuk/ABA-1788-2020
dc.authorwosid Tureli, Didem/KFS-7253-2024
dc.authorwosid Tureli, Ufuk/AAC-1815-2020
dc.contributor.author Khiarullah, Ali Kamil
dc.contributor.author Tureli, Ufuk
dc.contributor.author Kivanc, Didem
dc.date.accessioned 2024-05-25T11:41:11Z
dc.date.available 2024-05-25T11:41:11Z
dc.date.issued 2019
dc.department Okan University en_US
dc.department-temp [Khiarullah, Ali Kamil; Tureli, Ufuk] Yildiz Tech Univ, Elect & Commun Engn, TR-34220 Istanbul, Turkey; [Kivanc, Didem] Okan Univ, Elect & Elect Engn, TR-34959 Istanbul, Turkey en_US
dc.description Tureli, Ufuk/0000-0002-4986-5270; Tureli, Didem/0000-0001-6835-2940; en_US
dc.description.abstract During last two decades, power adaptation and beamforming solutions have been proposed for multiple input multiple output (MIMO) Ad Hoc networks. Game theory based methods such as cooperative and non-cooperative joint beamforming and power control for the MIMO ad hoc systems consider the interference and overhead reduction, but have failed to achieve the trade-off between communication overhead and power minimization. Cooperative method using game theory achieves the power minimization, but introduced the overhead. The non-cooperative solution using game theory reduced the overhead, but it takes more power and iterations for convergence. In this paper, a novel game theory based algorithms proposed to achieve the trade-off between power control and communication overhead for multiple antennas enabled wireless ad-hoc networks operating in multiple-users interference environment. The optimized joint iterative power adaption and beamforming method designed to minimize the mutual interference at every wireless node with constant received signal to interference noise ratio (SINR) at every receiver node. First cooperative potential game theory based algorithm designed for the power and interference minimization in which users cluster and binary weight books along used to reduce the overhead. Then the non-cooperative based approach using the reinforcement learning (RL) method is proposed to reduce the number of iterations and power consumption in networks, the proposed RL procedure is fully distributed as every transmit node require only an observation of its instantaneous beamformer label which can be obtained from its receive node. The simulation results of both methods prove the efficient power adaption and beamforming for small and large networks with minimum overhead and interference compared to state-of-art methods. (C) 2019 The Authors. Published by Atlantis Press SARL. en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.2991/ijcis.d.191121.001
dc.identifier.endpage 1445 en_US
dc.identifier.issn 1875-6891
dc.identifier.issn 1875-6883
dc.identifier.issue 2 en_US
dc.identifier.scopus 2-s2.0-85078228255
dc.identifier.scopusquality Q1
dc.identifier.startpage 1436 en_US
dc.identifier.uri https://doi.org/10.2991/ijcis.d.191121.001
dc.identifier.uri https://hdl.handle.net/20.500.14517/1494
dc.identifier.volume 12 en_US
dc.identifier.wos WOS:000515063600043
dc.identifier.wosquality Q3
dc.institutionauthor Kivanc D.
dc.language.iso en
dc.publisher Springernature en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 3
dc.subject Optimized intelligent design for smart systems en_US
dc.subject Wireless network beamforming en_US
dc.subject Power adaption en_US
dc.subject Interference en_US
dc.subject Strategic decision-making en_US
dc.subject Game theory en_US
dc.subject Reinforcement learning en_US
dc.title Optimized Intelligent Design for Smart Systems Hybrid Beamforming and Power Adaptation Algorithms for Sensor Networks Decision-Making Approach en_US
dc.type Article en_US
dc.wos.citedbyCount 0

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