Transmit Antenna Selection for Large-Scale MIMO GSM With Machine Learning

Gecgel, Selen; Goztepe, Caner; Kurt, Güneş Karabulut

doi:10.1109/lwc.2019.2944179

Cited by 26 publications

(15 citation statements)

References 12 publications

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“…However, they can provide solutions concomitantly to the environmental changes instead of impracticable assumptions. The study in [9] evinces this by comparing learning-driven algorithms with classical Euclidean distancebased methods with a real-time dataset, including channel imperfections.…”

Section: B Estimation Errorsmentioning

confidence: 98%

A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies

Gecgel,

Goztepe,

Kurt

et al. 2021

Preprint

Self Cite

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Future communication systems must include extensive capabilities as they will embrace a vast diversity of devices and applications. Conventional physical layer decision mechanisms may not meet these requirements due to the frequent use of impracticable and oversimplifying assumptions that lead to a trade-off between complexity and efficiency. By utilizing past experiences, learning-driven designs are promising solutions to present a resilient decision mechanism and provide a quick response even under exceptional circumstances. The corresponding design solutions should evolve following the learning-driven paradigms that offer increased autonomy and robustness. This evolution must take place by considering the facts of real-world systems without restraining assumptions. This paper introduces the common assumptions in the physical layer to highlight their discrepancies with practical systems. As a solution, learning algorithms are examined by considering implementation steps and challenges. Additionally, these issues are discussed through a real-time case study that uses software-defined radio nodes, demonstrating the potential performance improvement. A remedial perspective is presented to guide future studies.

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Section: B Estimation Errorsmentioning

confidence: 98%

A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies

Gecgel,

Goztepe,

Kurt

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Especially in multiple-input multiple-output (MIMO) technology, several developments utilizing ML in efficient transmit antennae selection are visible. To select a suitable antenna subset for large-scale MIMO systems, a study [70] proposed a dynamic generalized spatial modulation framework with Euclidean distance-optimized antenna selection (EDAS) and a Multi-layer perception (MLP) model, which enabled higher diversity gain. Another article [71] proposed a CNN-based transmit antenna selection for a nonorthogonal multiple-access MIMO system for 5G applications, where the proposed algorithm showed a performance 10,000 times faster than the exhaustive search and 2 times faster than the hyper region proposal network (HRPN) with an 89% validation accuracy.…”

Section: Antenna Selection Applicationsmentioning

confidence: 99%

Application of Machine Learning in Electromagnetics: Mini-Review

et al. 2021

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As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.

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“…Neural networks are very effective tools used in various design steps of analog and RF circuits [43][44][45]. They are as a 'black-box' modeling and are exerted in modeling of onchip inductors [46], semiconductor devices [47], conventional analog circuit building blocks [48,49], analog IC sizing [6,50], and in crucial RF circuit blocks such as power amplifiers [51], RF front-end receivers [52], low noise amplifiers [53], voltage-controlled oscillators [54,55], and multiple-input multiple-output (MIMO) systems [56,57].…”

Section: Neural Network Techniquementioning

confidence: 99%

Optimization techniques for analog and RF circuit designs: an overview

Kouhalvandi

Ceylan²,

Özoğuz

2020

Analog Integr Circ Sig Process

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Optimizations have gained much consideration from the researchers working in the domains of analog and radio frequency (RF), recently. Dealing with highly nonlinear behavior of active components and aiming to meet design specifications are common issues in all nonlinear circuit designs. As a consequence and accordingly, many studies have been conducted on diverse optimization methods and algorithms for tackling the design problems and meeting optimal solutions with high accuracy. The main purpose of this article is to provide a comprehensive and systematic literature review for the optimization approaches applied by the researchers for designing various analog and microwave circuits. We focus on considering the existing optimization methods from the newly published optimization methods in the last decade. Thus, this study can guide and enlighten complementary metal-oxide-semiconductor analog and RF microwave circuit designers to consider optimization methods commonly used in both areas and to expand conventional performance figures used in their area.

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Transmit Antenna Selection for Large-Scale MIMO GSM With Machine Learning

Cited by 26 publications

References 12 publications

A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies

A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies

Application of Machine Learning in Electromagnetics: Mini-Review

Optimization techniques for analog and RF circuit designs: an overview

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