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Conference Object Citation - WoS: 1Characterization of Satellite Transponder Impairments Based on Simulations with Test Data(Ieee, 2015) Ulubey, Orhan; Gulgonul, Senol; Kara, AliA satellite transponder simulator based on actual test data of TURKSAT 3A satellite has been developed to analyze degradation in multicarrier scenarios. Communication impairment sources through a transponder are explained in conjunction with a methodology defined to characterize total degradation resulting from them. Several transponder utilization scenarios are studied with respect to total degradation and optimum operation conditions are demonstrated.Conference Object An Overview of Challenges To Long-Term Sustainability and Scalability of Radio Frequency Fingerprinting(IEEE, 2024) Demiroglu, Harun Senol; Awan, Maaz Ali; Kara, AliInternet of Things (IoT) technology has become ubiquitous with a broad spectrum of applications. This vast penetration entails formidable cyber-security for the stable operation of the associated systems. Most inexpensive IoT devices employ rudimentary cryptographic security mechanisms due to their resource-limited architecture. Radio frequency fingerprinting (RFF) is a physical layer security mechanism that leverages hardware impairments for authentication and device classification. To this end, its scope has been limited to academia owing to daunting challenges. In this work, an abridged overview of the state-of-the-art is provided, along with a summary of the challenges that hinder progress toward practical applications. The article culminates with a discussion on the intricacies of performance metrics in RFF and the direction for future research.Conference Object A remote laboratory for training in radio communications(Ieee, 2007) Kara, Ali; Aydin, Elif Uray; Oektem, Rusen; Cagiltay, NergizThis paper presents, first, a short survey of remote laboratory initiatives in electrical and computer engineering, and then discusses design and development phases of remote laboratory environment on radio communications, the ERRL (European Remote Radio Laboratory). As being the first. attempt in establishing of such a large scale remote laboratory on radio communications, ERRL enables access to high technology RF equipments and setups through the Internet. The software structure, target groups and experimental set ups of ERRL are shortly discussed. First attempts on implementation of pilot experiments are discussed.Conference Object Pilot remote experiment in the ERRL(Ieee, 2007) Oezen, Mustafa; Aydin, Elif Uray; Kara, AliA remote laboratory platform enables the learners to access physical instruments at distant location and to perform experiments remotely via the Internet. This paper focuses on a pilot remote laboratory experiment on measurement of scattering parameters (s parameters) by using vector network analyzer (VNA) in the ERRL (European Remote Radio Laboratory) platform. The results of the pilot experiment are presented, and discussed shortly.Article Otonom Araç Radarları için 79 GHz Fazlı Mikroşerit Anten Dizisinin Besleme Analizi(2025) Dalveren, Yaser; Kara, Ali; Yılmaz, SelenOtomotiv radarı, güvenilirliği nedeniyle otonom araçlarda umut vadeden bir algılama teknolojisi olarak bilinmektedir. Günümüz otonom araçlarında, 77 – 81 GHz frekans bandı otomotiv radarları için ana çalışma bandıdır. Otomotiv radarlarının verimli çalışabilmesi için radar anteninin son derece hassas olması gerekir. Ancak, yüksek çalışma frekansları, yüksek kazanç, geniş bant genişliği ve düşük yan lob seviyeleri (SLL) gerektiren radar anteni tasarımında zorluklar ortaya çıkarabilmektedir. Bu sorunu ele almak için, bu çalışmada, eş düzlemli boşluk kaynak portu, dikey toprak köprüsü ve dalga portu dahil olmak üzere üç farklı topraklanmış eş düzlemli dalga kılavuzu (GCPW) besleme konfigürasyonu kullanılarak düzlemsel seri beslemeli doğrusal bir anten dizisinin 79 GHz otomotiv radar uygulamalarına uyarlaması amaçlanmaktadır. Antenin besleme yapılandırmalarıyla performansını değerlendirmek için benzetimler yürütülmüştür. Elde edilen sonuçlara göre, dalga portu beslemeli antenin en iyi empedans bant genişliğini (>3 GHz) elde ettiği, eş düzlemli boşluk kaynak portu veya dikey toprak köprüsü konfigürasyonları beslemeli antenin ise daha iyi ana lob faz merkezlemesi ve daha yüksek bir kazanç (>18,4 dBi) sergilediği, yan lob seviyelerinin (SLL) -16,28 dB’nin altında olduğu gösterilmiştir. Bu bulguların, yeni nesil otonom araçlar için yüksek performanslı radar antenlerinin geliştirilmesine katkıda bulunabileceği düşünülmektedir.Conference Object Citation - WoS: 1Citation - Scopus: 1Optimizing Radio Frequency Fingerprinting for Device Classification: a Study Towards Lightweight Dl Models(IEEE, 2024) Iyiparlakoglu, Raif; Awan, Maaz Ali; Dalveren, Yaser; Kara, AliAs the Internet of Things (IoT) permeates diverse application domains, ensuring the security of wireless networks has become increasingly critical. However, the constraints of resource-limited IoT devices render complex encryption impractical. Consequently, Radio Frequency Fingerprinting (RFF) has emerged as a promising avenue, leveraging unique device characteristics resulting from manufacturing nonlinearities. RFF enhances physical layer security by enabling device classification and authentication at IoT gateways. While deep learning (DL) aided RFF systems offer exceptional classification accuracy, their deployment on edge devices remains challenging to this end. Accordingly, there is a gap in the literature for efficient model exploration and implementation. This study proposes a lightweight Convolutional Neural Network (CNN) model using 1D convolutional filters to reduce inference latency. The model was applied to an open-source dataset comprising 30 LoRa devices. An evaluation was conducted to compare classification accuracy and inference latency using Short Time Fourier Transform (STFT) and Fast Fourier Transform (FFT) for preprocessing. Additionally, the performance of the proposed model was compared against a CNN model utilizing 2D convolutional filters. The model exhibited a significant reduction in inference latency with miniscule degradation in classification accuracy, addressing the identified gap, and propelling the academic discourse towards RFF for edge devices.
