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Article Citation - WoS: 4Citation - Scopus: 6On the Classification of Modulation Schemes Using Higher Order Statistics and Support Vector Machines(Springer, 2022) Coruk, Remziye Busra; Gokdogan, Bengisu Yalcinkaya; Benzaghta, Mohamed; Kara, AliThe recognition of modulation schemes in military and civilian applications is a major task for intelligent receiving systems. Various Automatic Modulation Classification (AMC) algorithms have been developed for this purpose in the literature. However, classification with low computational complexity as well as reasonable processing time is still a challenge. In this paper, a feature-based approach along with various classifiers is employed based on statistical features as well as higher-order moments and cumulants. An over-the-air (OTA) recorded dataset consisting of four analog and ten digital modulation schemes are used for testing the proposed method at 0-20 dB SNR. The overall accuracy for quadratic Support Vector Machine (SVM) is found to be as high as 98% at 10 dB. The comparison of the results with other AMC papers published in the literature indicates that the proposed method present higher accuracy, especially for realistic channel induced OTA dataset.Article Citation - WoS: 5Citation - Scopus: 8Optimal Operation Mode Selection for Energy-Efficient Light-Weight Multi-Hop Time Synchronization in Linear Wireless Sensor Networks(Springer, 2020) Al Imran, Md Abdullah; Dalveren, Yaser; Tavli, Bulent; Kara, AliWe explored the joint effect of synchronization window and offset/drift mode selection on the time synchronization of linear wireless sensor networks (LWSNs). Recent advances in the field along with the availability of capable hardware led to adoption of LWSNs in diverse areas like monitoring of roads, pipelines, and tunnels. The linear topology applications are susceptible to single point of failure; therefore, energy efficient operation of LWSNs is even more important than the traditional WSNs. To address the challenge, we investigate the time synchronization mode selection for the optimum operation of a multi-hop and low-overhead LWSN. We investigate two modes of synchronization: synchronization by using only offset and synchronization by using offset in addition to the clock drift. Furthermore, we investigate the effects of synchronization window size. Our experimental results reveal that computation of offset alone for smaller window sizes and resynchronization periods is sufficient in achieving acceptable degree of synchronization.Article Citation - WoS: 5Citation - Scopus: 5Hierarchical Classification of Analog and Digital Modulation Schemes Using Higher-Order Statistics and Support Vector Machines(Springer, 2024) Yalcinkaya, Bengisu; Coruk, Remziye Busra; Kara, Ali; Tora, HakanAutomatic modulation classification (AMC) algorithms are crucial for various military and commercial applications. There have been numerous AMC algorithms reported in the literature, most of which focus on synthetic signals with a limited number of modulation types having distinctive constellations. The efficient classification of high-order modulation schemes under real propagation effects using models with low complexity still remains difficult. In this paper, employing quadratic SVM, a feature-based hierarchical classification method is proposed to accurately classify especially higher-order modulation schemes and its performance is investigated using over the air (OTA) collected data. Statistical features, higher-order moments, and higher-order cumulants are utilized as features. Then, the performances of some well-known classifiers are evaluated, and the classifier presenting the best performance is employed in the proposed hierarchical classification model. An OTA dataset containing 17 analog and digital modulation schemes is used to assess the performance of the proposed classification model. With the proposed hierarchical classification algorithm, a significant improvement has been achieved, especially in higher-order modulation schemes. The overall accuracy with the proposed hierarchical structure is 96% after 5 dB signal-to-noise ratio value, approximately a 10% increase is achieved compared to the traditional classification algorithm.Article Citation - WoS: 18Citation - Scopus: 19Performance Analysis of Modular Rf Front End for Rf Fingerprinting of Bluetooth Devices(Springer, 2020) Uzundurukan, Emre; Ali, Aysha M.; Dalveren, Yaser; Kara, AliRadio frequency fingerprinting (RFF) could provide an efficient solution to address the security issues in wireless networks. The data acquisition system constitutes an important part of RFF. In this context, this paper presents an implementation of a modular RF front end system to be used in data acquisition for RFF. Modularity of the system provides flexible implementation options to suit diverse frequency bands with different applications. Moreover, the system is able to collect data by means of any digitizer, and enable to record the data at lower frequencies. Therefore, proposed RF front end system becomes a low-cost alternative to existing devices used in data acquisition. In its implementation, Bluetooth (BT) signals were used. Initially, transients of BT signals were detected by utilizing a large number of BT devices (smartphones). From the detected transients, distinctive signal features were extracted. Then, support vector machine (SVM) and neural networks (NN) classifiers were implemented to the extracted features for evaluating the feasibility of proposed system in RFF. As a result, 96.9% and 96.5% classification accuracies on BT devices have been demonstrated for SVM and NN classifiers respectively.
