Köse, Abdulkadir

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Profile URL
https://hdl.handle.net/20.500.12573/2602
Name Variants
Abdulkadir KÖSE
Kose, Abdulkadir
Koset, Abdulkadir
Köse, Abdulkadir
Job Title
Dr. Öğr. Üyesi
Email Address
abdulkadir.kose@agu.edu.tr
Main Affiliation
02. 04. Bilgisayar Mühendisliği
Status
Current Staff
Website
ORCID ID
0000-0002-6877-1392
Scopus Author ID
56810884500
Turkish CoHE Profile ID
356146
Google Scholar ID
eRwzjO4AAAAJ
WoS Researcher ID

Sustainable Development Goals

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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1

Research Products

11

SUSTAINABLE CITIES AND COMMUNITIES
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1

Research Products
Documents

14

Citations

120

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7

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Scholarly Output

7

Articles

3

Views / Downloads

13/0

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

25

Scopus Citation Count

35

WoS h-index

2

Scopus h-index

3

Patents

0

Projects

0

WoS Citations per Publication

3.57

Scopus Citations per Publication

5.00

Open Access Source

3

Supervised Theses

0

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JournalCount
Sensors2
-- 13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024 -- Hybrid, Mataram -- 2066361
-- 16th IEEE Malaysia International Conference on Communication, MICC 2023 -- Kuala Lumpur -- 1972441
7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications-ICHORA -- MAY 23-24, 2025 -- Ankara, TURKIYE1
97th IEEE Vehicular Technology Conference (VTC-Spring) -- JUN 20-23, 2023 -- Florence, ITALY1
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Scholarly Output Search Results

Now showing 1 - 7 of 7
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    Conference Object
    Citation - Scopus: 1
    Semantic-Forward Relaying for 6G: Performance Boosts With ResNet-18 and GoogleNet Plus
    (Institute of Electrical and Electronics Engineers Inc., 2024) Erkantarci, Betul; Çoban, Mert Korkut; Bozoǧlu, Abdulkadir; Köse, Abdulkadir
    This paper investigates the integration of advanced deep learning architectures, namely ResNet-18, GoogleNet and enhanced GoogleNet (GoogleNet Plus), into the Semantic-Forward (SF) relaying framework for cooperative communications in 6G networks. The SF relaying framework enhances transmission efficiency and robustness by leveraging semantic information at relay nodes. We analyze and compare the performance of these deep learning models in terms of validation accuracy, semantic accuracy, and Euclidean distance (ED) metrics on the CIFAR-10 dataset. Results indicate that ResNet-18 achieves the highest performance due to its residual learning architecture. GoogleNet Plus, incorporating Automatic Mixed Precision (AMP) training and the Adam optimizer, demonstrates improved stability and efficiency compared to the original GoogleNet. The results highlights the potential of deep learning models to enhance semantic processing capabilities in SF relaying, contributing to the development of more efficient, resilient, and adaptive cooperative communication systems in 6G networks. © 2025 Elsevier B.V., All rights reserved.
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    Article
    Citation - WoS: 16
    Citation - Scopus: 19
    Recent Advances in Machine Learning for Network Automation in the O-RAN
    (MDPI, 2023) Hamdan, Mutasem Q.; Lee, Haeyoung; Triantafyllopoulou, Dionysia; Borralho, Ruben; Kose, Abdulkadir; Amiri, Esmaeil; Tafazolli, Rahim
    The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.
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    Conference Object
    A Comprehensive Investigation into Strip Steel Defect Detection Using Traditional Machine Learning and Deep Learning Models
    (IEEE, 2025) Erkantarci, Betul; Kurban, Rifat; Bakal, Mehmet Gokhan; Kose, Abdulkadir
    The steel manufacturing sector places great importance on guaranteeing the quality of strip steel products, which has led to a thorough investigation of defect detection approaches. This work conducts a comparative analysis of traditional machine learning and deep learning models to determine their efficacy in detecting defects in strip steel. Our analysis is based on a dataset that includes a variety of images of strip steel surfaces showing different types of defects. In this work, we adopt image preprocessing techniques to improve the quality of input images prior to the application of classification methods. We employ traditional ML algorithms including Support Vector Machine and Random Forest, and deep learning model AlexNet Convolutional Neural Networks for effective defect classification. Consequently, we present comparative evaluations that highlight the strengths and weaknesses of each approach, considering accuracy scores.
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    Conference Object
    Machine Learning Based Beamwidth Adaptation for mmWave Vehicular Communications
    (Institute of Electrical and Electronics Engineers Inc., 2023) Manic, Setinder; Heng Foh, Chuan; Köse, Abdulkadir; Lee, Haeyoung; Leow, Chee Yen; Chatzimisios, Periklis; Suthaputchakun, Chakkaphong
    The incorporation of mmWave technology in vehicular networks has unlocked a realm of possibilities, propelling the advancement of autonomous vehicles, enhancing interconnectedness, and facilitating communication for intelligent transportation systems (ITS). Despite these strides in connectivity, challenges such as high path-loss have arisen, impacting existing beam management procedures. This work aims to address this issue by improving beam management techniques, specifically focusing on enhancing the service time between vehicles and base stations through adaptive mmWave beamwidth adjustments, accomplished using a Contextual Multi-Armed Bandit Algorithm. By leveraging various conditions to train the ML agent of the Contextual Multi-Armed Bandit Algorithm, it seeks to learn about vehicle mobility profiles and optimize the usage of different antenna beamwidth settings to maximize seamless connection time. The extensive simulation results showcase the effectiveness of an adaptive beamwidth for mobility profiles, extending the connection time a vehicle experiences with a base station when compared to the existing strategies. © 2024 Elsevier B.V., All rights reserved.
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    Article
    Citation - WoS: 8
    Citation - Scopus: 10
    Multi-Agent Context Learning Strategy for Interference-Aware Beam Allocation in mmWave Vehicular Communications
    (IEEE-Inst Electrical Electronics Engineers Inc, 2024) Kose, Abdulkadir; Lee, Haeyoung; Foh, Chuan Heng; Shojafar, Mohammad
    Millimeter wave (mmWave) has been recognized as one of key technologies for 5G and beyond networks due to its potential to enhance channel bandwidth and network capacity. The use of mmWave for various applications including vehicular communications has been extensively discussed. However, applying mmWave to vehicular communications faces challenges of high mobility nodes and narrow coverage along the mmWave beams. Due to high mobility in dense networks, overlapping beams can cause strong interference which leads to performance degradation. As a remedy, beam switching capability in mmWave can be utilized. Then, frequent beam switching and cell change become inevitable to manage interference, which increase computational and signalling complexity. In order to deal with the complexity in interference control, we develop a new strategy called Multi-Agent Context Learning (MACOL), which utilizes Contextual Bandit to manage interference while allocating mmWave beams to serve vehicles in the network. Our approach demonstrates that by leveraging knowledge of neighbouring beam status, the machine learning agent can identify and avoid potential interfering transmissions to other ongoing transmissions. Furthermore, we show that even under heavy traffic loads, our proposed MACOL strategy is able to maintain low interference levels at around 10%.
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    Conference Object
    Citation - WoS: 1
    Citation - Scopus: 5
    Contextual Multi-Armed Bandit Based Beam Allocation in mmWave V2x Communication Under Blockage
    (IEEE, 2023) Cassillas, Arturo Medina; Koset, Abdulkadir; Leet, Haeyoung; Foh, Chuan Heng; Leowt, Chee Yen
    Due to its low latency and high data rates support, mmWave communication has been an important player for vehicular communication. However, this carries some disadvantages such as lower transmission distances and inability to transmit through obstacles. This work presents a Contextual Multi-Armed Bandit Algorithm based beam selection to improve connection stability in next generation communications for vehicular networks. The algorithm, through machine learning (ML), learns about the mobility contexts of the vehicles (location and route) and helps the base station make decisions on which of its beam sectors will provide connection to a vehicle. In addition, the proposed algorithm also smartly extends, via relay vehicles, beam coverage to outage vehicles which are either in NLOS condition due to blockages or not served any available beam. Through a set of experiments on the city map, the effectiveness of the algorithm is demonstrated, and the best possible solution is presented.
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    Article
    Context-Aware Beam Selection for IRS-Assisted Mmwave V2I Communications
    (MDPI, 2025) Suarez del Valle, Ricardo; Kose, Abdulkadir; Lee, Haeyoung
    Millimeter wave (mmWave) technology, with its ultra-high bandwidth and low latency, holds significant promise for vehicle-to-everything (V2X) communications. However, it faces challenges such as high propagation losses and limited coverage in dense urban vehicular environments. Intelligent Reflecting Surfaces (IRSs) help address these issues by enhancing mmWave signal paths around obstacles, thereby maintaining reliable communication. This paper introduces a novel Contextual Multi-Armed Bandit (C-MAB) algorithm designed to dynamically adapt beam and IRS selections based on real-time environmental context. Simulation results demonstrate that the proposed C-MAB approach significantly improves link stability, doubling average beam sojourn times compared to traditional SNR-based strategies and standard MAB methods, and achieving gains of up to four times the performance in scenarios with IRS assistance. This approach enables optimized resource allocation and significantly improves coverage, data rate, and resource utilization compared to conventional methods.