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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Multi-scale deformable alignment and content-adaptive inference for flexible-rate bi-directional video compression(IEEE Computer Society, 2023) Department of Electrical and Electronics Engineering; Yılmaz, Mustafa Akın; Ulaş, Ökkeş Uğur; Tekalp, Ahmet Murat; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of EngineeringThe lack of ability to adapt the motion compensation model to video content is an important limitation of current end-to-end learned video compression models. This paper advances the state-of-the-art by proposing an adaptive motion-compensation model for end-to-end rate-distortion optimized hierarchical bi-directional video compression. In particular, we propose two novelties: i) a multi-scale deformable alignment scheme at the feature level combined with multi-scale conditional coding, ii) motion-content adaptive inference. In addition, we employ a gain unit, which enables a single model to operate at multiple rate-distortion operating points. We also exploit the gain unit to control bit allocation among intra-coded vs. bi-directionally coded frames by fine tuning corresponding models for truly flexible-rate learned video coding. Experimental results demonstrate state-of-the-art rate-distortion performance exceeding those of all prior art in learned video coding1.Publication Metadata only Ris-aided angular-based hybrid beamforming design in mmwave massive mimo systems(IEEE, 2022) Koc, Asil; Tho Le-Ngoc; Department of Electrical and Electronics Engineering; Yıldırım, İbrahim; Başar, Ertuğrul; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of EngineeringThis paper proposes a reconfigurable intelligent surface (RIS)-aided and angular-based hybrid beamforming (AB-HBF) technique for the millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The proposed RIS-AB-HBF architecture consists of three stages: (i) RF beam-former, (ii) baseband (BB) precoder/combiner, and (iii) RIS phase shift design. First, in order to reduce the number of RF chains and the channel estimation overhead, RF beamformers are designed based on the 3D geometry-based mmWave channel model using slow time-varying angular parameters of the channel. Second, a BB precoder/combiner is designed by exploiting the reduced-size effective channel seen from the BB stages. Then, the phase shifts of the RIS are adjusted to maximize the achievable rate of the system via the nature-inspired particle swarm optimization (PSO) algorithm. Illustrative simulation results demonstrate that the use of RISs in the AB-HBF systems has the potential to provide more promising advantages in terms of reliability and flexibility in system design.Publication Metadata only A novel reconfigurable intelligent surface-supported code index modulation-based receive spatial modulation system(IEEE-Institute of Electrical and Electronics Engineers, 2024) Ozden, Burak Ahmet; Cogen, Fatih; Aydin, Erdogan; Ilhan, Haci; Wen, Miaowen; Department of Electrical and Electronics Engineering; Başar, Ertuğrul; Department of Electrical and Electronics Engineering; College of EngineeringToday's wireless communication networks have many requirements such as high data rate, high reliability, low latency, low error data transmission, and high energy efficiency. High-performance index modulation (IM) techniques and reconfigurable intelligent surface (RIS) technology, which has recently attracted the attention of researchers, are strong candidates to meet these requirements. This paper introduces a novel RIS-supported code IM-based receive spatial modulation (RIS-CIM-RSM) system. The proposed RIS-CIM-RSM system uses quadrature amplitude modulation (QAM) symbols, receive antenna indices, and spreading code indices for wireless data transmission. In the proposed system, an RIS applies a phase rotation that maximizes signal-to-noise ratio (SNR) to the signals coming to the reflecting elements and directs them to the selected receive antenna. Performance analyses of the proposed RIS-CIM-RSM system such as data rate, throughput, and energy saving are obtained. The results obtained show that the proposed RIS-CIM-RSM system is superior to the counterpart RIS-based IM systems in the literature in terms of data rate, throughput, energy saving, and error performance.Publication Metadata only Next generation multiple access for 6G(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Fang, Fang; Liu, Yuanwei; Dhillon, Harpreet S. S.; Wu, Yiqun; Ding, Zhiguo; Department of Electrical and Electronics Engineering; Başar, Ertuğrul; Department of Electrical and Electronics Engineering; ; College of Engineering;With the standardization of 5G systems, research focus is slowly shifting towards potential designs, use cases, and performance targets for 6G systems. To meet the escalating data demands of mobile devices and to deal with the deluge of data, as well as the high-rate connectivity required by bandwidth-thirsty applications (e.g., space-air-ground-integrated-networks (SAGINs), augmented reality (AR), and virtual reality (VR), etc.), 6G networks are expected to provide substantial breakthroughs beyond the previous five generations.Publication Metadata only Edge computing in future wireless networks: a comprehensive evaluation and vision for 6G and beyond(Korean Institute of Communications and Information Sciences, 2024) Ergen, Mustafa; Saoud, Bilal; Shayea, Ibraheem; El-Saleh, Ayman A.; İnan, Feride; Tüysüz, Mehmet Fatih; Department of Electrical and Electronics Engineering; Ergen, Onur; Department of Electrical and Electronics Engineering; ; College of Engineering;Future internet aims to function as a neutral in-network storage and computation platform, essential for enabling 6G and beyond wireless use cases. Information-Centric Networking and Edge Computing are key paradigms driving this vision by offering diversified services with fast response times across heterogeneous networks. This approach requires effective coordination to dynamically utilize resources like links, storage, and computation in near real-time within a non-homogenous and distributed computing environment. Additionally, networks must be aware of resource availability and reputational information to manage unknown and partially observed dynamic systems, ensuring the desired Quality of Experience (QoE). This paper provides a comprehensive evaluation of edge computing technologies, starting with an introduction to its architectural frameworks. We examine contemporary research on essential aspects such as resource allocation, computation delegation, data administration, and network management, highlighting existing research gaps. Furthermore, we explore the synergy between edge computing and 5G, and discuss advancements in 6G that enhance solutions through edge computing. Our study emphasizes the importance of integrating edge computing in future considerations, particularly regarding sustainable energy and standards. © 2024 The Author(s)Publication Metadata only Kirchhoff meets Johnson: in pursuit of unconditionally secure communication(WILEY, 2024) Department of Electrical and Electronics Engineering; Başar, Ertuğrul; Department of Electrical and Electronics Engineering; College of EngineeringNoise: an enemy to be dealt with and a major factor limiting communication system performance. However, what if there is gold in that garbage? In conventional engineering, our focus is primarily on eliminating, suppressing, combating, or even ignoring noise and its detrimental impacts. Conversely, could we exploit it similarly to biology, which utilizes noise-alike carrier signals to convey information? In this context, the utilization of noise, or noise-alike signals in general, has been put forward as a means to realize unconditionally secure communication systems in the future. In this tutorial article, we begin by tracing the origins of thermal noise-based communication and highlighting one of its significant applications for ensuring unconditionally secure networks: the Kirchhoff-law-Johnson-noise (KLJN) secure key exchange scheme. We then delve into the inherent challenges tied to secure communication and discuss the imperative need for physics-based key distribution schemes in pursuit of unconditional security. Concurrently, we provide a concise overview of quantum key distribution schemes and draw comparisons with their KLJN-based counterparts. Finally, extending beyond wired communication loops, we explore the transmission of noise signals over-the-air and evaluate their potential for stealth and secure wireless communication systems.Publication Metadata only Correlative information maximization: a biologically plausible approach to supervised deep neural networks without weight symmetry(Neural Information Processing Systems 36, 2023) Pehlevan, Cengiz; Department of Electrical and Electronics Engineering; Bozkurt, Barışcan; Erdoğan, Alper Tunga; Department of Electrical and Electronics Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Graduate School of Sciences and Engineering; College of EngineeringThe backpropagation algorithm has experienced remarkable success in training large-scale artificial neural networks;however, its biological plausibility has been strongly criticized, and it remains an open question whether the brain employs supervised learning mechanisms akin to it. Here, we propose correlative information maximization between layer activations as an alternative normative approach to describe the signal propagation in biological neural networks in both forward and backward directions. This new framework addresses many concerns about the biological-plausibility of conventional artificial neural networks and the backpropagation algorithm. The coordinate descent-based optimization of the corresponding objective, combined with the mean square error loss function for fitting labeled supervision data, gives rise to a neural network structure that emulates a more biologically realistic network of multi-compartment pyramidal neurons with dendritic processing and lateral inhibitory neurons. Furthermore, our approach provides a natural resolution to the weight symmetry problem between forward and backward signal propagation paths, a significant critique against the plausibility of the conventional backpropagation algorithm. This is achieved by leveraging two alternative, yet equivalent forms of the correlative mutual information objective. These alternatives intrinsically lead to forward and backward prediction networks without weight symmetry issues, providing a compelling solution to this long-standing challenge.Publication Metadata only Machine learning aided NR-V2X quality of service predictions(IEEE, 2023) Karaağaç, Sercan; Department of Electrical and Electronics Engineering; Reyhanoğlu, Aslıhan; Kar, Emrah; Kümeç, Feyzi Ege; Kara, Yahya Şükür Can; Turan, Buğra; Ergen, Sinem Çöleri; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; Koc University Ford Otosan Automotive Technologies Laboratory (KUFOTAL)Vehicle-to-Everything Communication (V2X) technologies aim to meet strict quality-of-service (QoS) requirements of vehicular connectivity applications such as safety message exchange, remote driving, and sensor data sharing. The high reliability requirement is particularly important to enable safety relevant applications. Thus, predicting QoS levels becomes key to ensure the reliability of the connected vehicle applications. Recently, machine learning (ML) algorithms are demonstrated to provide dependable predictions to plan, simulate, and evaluate the performance of vehicular networks. In this paper, we propose ML aided New Radio (NR)-V2X QoS predictions scheme to provide Packet Delivery Ratio (PDR) and throughput predictions with the input of Modulation and Coding Schemes (MCS), distance-to-base station, Signal to Interference plus Noise Ratio (SINR), and packet size. Seven different ML algorithms based prediction models are trained and evaluated by using NR-V2X simulation data. We provide performance comparisons between Support Vector Regression (SVR), Deep Neural Network (DNN), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Light GBM (LGBM) for predicting throughput and PDR. We demonstrate that CatBoost and RF are the best performing algorithms to predict throughput and PDR of NR-V2X networks, respectively.Publication Metadata only Investigating the effect of body composition differences on seismocardiogram characteristics(IEEE Computer Soc, 2023) Tokmak, Fadime; Department of Electrical and Electronics Engineering; Gürsoy, Beren Semiz; Department of Electrical and Electronics Engineering; College of EngineeringIn seismocardiogram (SCG) analysis, inter-subject variability is observed as the medium between the heart and accelerometer consists of different tissues made of bone, muscle, fat and skin cells of which combination varies across different people. Anatomically, a similar pattern is present in the speech production system, where the vocal cord and vocal tract are considered as the source and medium, respectively. For observing the change of the vocal tract filter while voicing different sounds, linear predictive analysis has been used for years. Thus, it was hypothesized that the medium characteristics of the human thorax would also have a filtering effect on the SCG signals and the differences in the filtering effects would be observed in the respiration (<1 Hz), vibration (1-20 Hz) and acoustic (>20 Hz) characteristics of the SCG signals. To that aim, three different binary classification tasks representing the body composition differences were defined: (i) whether the metabolic age of the subject is more than the real age of the subject, (ii) whether the BMI of the subject is bigger than 25, and (iii) whether the subject is male or female. To understand the metabolism-induced changes in the respiration, vibration and acoustic components, classification experiments were conducted using different frequency bands of the SCG signal. In each case, linear predictive coefficients were extracted and used to train individual classification models for the aforementioned scenarios. With the vibration components (120 Hz), all of the tasks resulted in high performance (0.86, 0.93, 0.93) for age, BMI and gender classification tasks, respectively. This study reveals that the vibration components of SCG make a stable and informative contribution to selected classification tasks, and due to its high generalizability, it is suitable for various practical applications.Publication Metadata only Flexible-rate learned hierarchical bi-directional video compression with motion refinement and frame-level bit allocation(IEEE, 2022) Department of Electrical and Electronics Engineering; Çetin, Eren; Yılmaz, Mustafa Akın; Tekalp, Ahmet Murat; Department of Electrical and Electronics Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); College of Engineering; Graduate School of Sciences and EngineeringThis paper presents improvements and novel additions to our recent work on end-to-end optimized hierarchical bidirectional video compression [1] to further advance the state-of-the-art in learned video compression. As an improvement, we combine motion estimation and prediction modules and compress refined residual motion vectors for improved rate-distortion performance. As novel addition, we adapted the gain unit proposed for image compression to flexible-rate video compression in two ways: first, the gain unit enables a single encoder model to operate at multiple rate-distortion operating points; second, we exploit the gain unit to control bit allocation among intra-coded vs. bi-directionally coded frames by fine tuning corresponding models for truly flexible-rate learned video coding. Experimental results demonstrate that we obtain state-of-the-art rate-distortion performance exceeding those of all prior art in learned video coding.