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ÖgeA new approach to satellite communication: Harnessing the power of reconfigurable intelligent surfaces(Graduate School, 2024-01-22)It is widely accepted that user-centric and ubiquitous connectivity, which are desired by both end users and operators for the 6th generation (6G) and beyond communication technologies, can be achieved through the unique orchestration of terrestrial and non-terrestrial networks (NTNs) in next-generation communication systems. This vision is also described by the 3rd Generation Partnership Project (3GPP) in Technical Report (TR) 38.821 for the operation of New Radio (NR) in NTNs. According to the definition by the 3GPP, an NTN basically consists of unmanned aerial vehicles, high-altitude platform stations (HAPS) systems, and dense satellite deployments. Low-Earth orbit (LEO) satellites and HAPS systems are considered to be the key enablers for NTNs due to their unique features, which include longer operating times and wider coverage areas. The most important pillars of non-terrestrial networks are ultra-dense satellite constellations. Although satellite networks are considered a prominent solution, many challenging open issues remain to be addressed. The most prominent ones are the size, weight, and power (SWaP) constraints, high path loss, and energy efficiency. As known, multi-antenna technologies are used to mitigate high path loss by taking advantage of its beamforming capacity. However, the hardware and signal processing units of multi-antenna systems are quite complex and costly. These costs are much higher in satellite networks. Recently, it was shown that a passive antenna solution with reconfigurable smart surfaces can reduce these costs and help increase communication performance. In this regard, we propose the use of reconfigurable intelligent surface (RIS) to improve coordination between these networks given that RISs perfectly match SWaP restrictions of operating in satellite networks as a main focus of this thesis. A comprehensive framework of RIS-assisted non-terrestrial and interplanetary communications is presented that pinpoints challenges, use cases, and open issues. Furthermore, the performance of RIS-assisted NTNs under environmental effects, such as solar scintillation and satellite drag, is discussed in light of simulation results. First, we propose a novel architecture involving the use of RIS units to mitigate the path loss associated with long transmission distances. These RIS units can be placed on satellite reflectarrays, and, when used in broadcasting and beamforming, it can provide significant gains in signal transmission. This study shows that RIS-assisted satellites can provide a severe improvement in downlink and achievable uplink rates for terrestrial networks. Although RIS has the potential to increase efficiency and perform complex signal processing over the transmission environment instead of transceivers, RIS needs information on the cascaded channel in order to adjust the phase of the incident signal. Consequently, channel estimation is an essential part of RIS-assisted communications. A study presented in the thesis evaluates the pilot signal as a graph. It incorporates this information into the graph attention networks (GATs) to track the phase relation through pilot signaling. The proposed GAT-based channel estimation method investigates the performance of the direct-to-satellite (DtS) networks for different RIS configurations to solve the challenging channel estimation problem. It is shown that the proposed GAT demonstrates a higher performance with increased robustness under changing conditions and has lower computational complexity compared to conventional deep learning (DL) methods. Moreover, based on the proposed method, bit error rate (BER) performance is investigated for RIS designs with discrete and non-uniform phase shifts under channel estimation. One of the findings in this study is that the channel models of the operating environment and the performance of the channel estimation method must be considered during RIS design to exploit performance improvement as far as possible. We show that RIS can improve energy efficiency in ground-to-satellite com munications. To complete the puzzle of overall satellite communications, we investigate RIS-assisted inter-satellite communication performance in terms of BER and achievable rate as well since broadband inter-satellite communication is one of the key elements of satellite communication systems that orchestrate massive satellite swarms in cooperation. Thanks to technological advancements in microelectronics and micro-systems, the terahertz (THz) band has emerged as a strong candidate for inter-satellite links (ISLs) due to its promise of wideband communication. In particular, multi-antenna systems can improve the system performance along with the wideband supported by the THz band. However, multi-antenna systems should be considered due to their SWaP constraints. On the other hand, as a state-of-the-art multi-antenna technology, RIS is able to relax SWaP constraints because of its passive component-based structures. However, as similar reflection characteristic throughout the wideband is challenging to meet, it is possible to observe beam misalignment. In the thesis, we first provide an assessment of the use of the THz band for ISLs and quantify the impact of misalignment fading on error performance. Then, to compensate for the high path loss associated with high carrier frequencies, and to further improve the signal-to-noise ratio (SNR), we propose using RISs mounted on neighboring satellites to enable signal propagation. Based on a mathematical analysis of the problem, we present the error rate expressions for RIS-assisted ISLs with misalignment fading. Also, numerical results show that RIS can leverage the error rate performance and achievable capacity of THz ISLs as long as a proper antenna alignment is satisfied. As the misalignment error seems one of the challenges on the path toward practical RIS-assisted NTN, the acquisition of a reliable direction of arrival (DoA) estimation becomes more of an issue in achieving promised improvements in RIS-assisted communication systems. For that reason, we address DoA estimation problem in RIS-assisted communication systems in the thesis. For this aim, we use a single-channel intelligent surface whose physical layer compression is achieved using a coded-aperture technique, probing the spectrum of far-field sources that are incident on the aperture using a set of spatiotemporally incoherent modes. This information is then encoded and compressed into the channel of the coded-aperture. The coded-aperture is based on a metasurface antenna design and it works as a receiver, exhibiting a single-channel and replacing the conventional multi-channel raster scan-based solutions for DoA estimation. The GAT network enables the compressive DoA estimation framework to learn the DoA information directly from the measurements acquired using the coded-aperture. This step eliminates the need for an additional reconstruction step and significantly simplifies the processing layer to achieve DoA estimation. We show that the presented GAT integrated single-pixel radar framework can retrieve high-fidelity DoA information even under relatively low signal-to-noise ratio (SNR) levels. Along with above work, in this thesis we analyse the performance of the main communication pillars of an end-to-end RIS-assisted satellite communication system and focus on the development of solutions to open problems that are essential in practical application.
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ÖgeDeep learning for inverse problems in imaging(Graduate School, 2024-02-15)Efforts to solve inverse imaging problems with deep learning techniques have increased the performance results of the algorithms. However, it has been observed that the increase in the performance of deep networks is mostly directly proportional to their more advanced and powerful architectural design. Acting with a pure architectural design leads researchers to dead end in the development of new solutions. On the other hand, in the classical era before deep learning, inverse imaging problems have been solved by making use of clean image models. Among model-based methods from classical period, the brightest results belong to the algorithms based on sparsity in transform domain. Contrary to this known fact, the common habit in deep learning literature to solve inverse problems is to find a model (map) on pixel domain rather than transform domain. Only a few studies have addressed training of deep networks in transform domain. In image denoising problem, deep networks that prefer training in transform domain have mostly chosen the discrete wavelet transform. The major factor in such a choice is that the wavelet transform produces image-like spectrum coefficients (subband images). Convolution layer is widely used in architecture of networks which are proposed for inverse imaging problems and it searches for a relationship between neighboring values of the input data of a convolution layer. In other words, it is reasonable to use wavelet transform coefficients in deep networks. Therefore, these wavelet-based networks have given effective results for inverse imaging problems. However, transforms such as DCT, which are known to provide good energy compaction property for most images in solving inverse problems, have not been preferred in deep networks. This is because they do not produce spectra such as wavelet subband images. In JPEG compression artifact removal problem, the primary source of compression artifact is the quantization of the transform coefficients of an image. During the quantization, transforms which have high compression ability such as the DCT are chosen. However, the majority of compression artifact removal algorithms have used deep neural networks that find a map in pixel domain. Based on these observations above, in this thesis study, novel transform based approaches are presented for image denoising and JPEG compression artifact removal problems. DCTNet is a deep convolutional neural network that utilizes the DCT for image denoising problem. In DCTNet, DCT coefficients of image patches extracted overlappingly from noisy image are calculated. Then, the spectral coefficients of all the patches are ordered to form a channel, which are suitable for subsequent processing in convolution layers. It has been shown mathematically that such a usage corresponds to the process of convolution of 2D DCT basis images with noisy image. Similarly, it has been shown that the calculation of inverse DCT coefficients can be done by a convolution operation with the same basis images. In this way, effective training of CNN networks in the DCT domain is carried out and it is shown that the proposed DCTNet give successful results in image denoising problem. Harmonic Nets are proposed by utilizing the DCTNet structure for JPEG compression artifact removal problem. In addition to the DCT, sine and Hartley transforms are also utilized to remove compression artifacts. These two transforms having high compression capability, which have not been discussed in the context of JPEG until now, are used in deep networks for the first time. Architectural changes have occurred in designing the proposed Harmonic Nets due to some differences between all three transforms. Experimental study have shown that although the proposed networks have fewer parameters and a simpler network topology, they surpass some of the advanced deep networks with the highest performance results and lag behind the others by a small margin. Within the scope of this thesis, compressed sensing MRI problem, which is a common technique in reconstructing magnetic resonance images, is also discussed. Over the past decade, it has been shown theoretically and empirically that the solution of additive white Gaussian noise removal problem is important not only for denoising problem but also for other inverse imaging problems. In plug-and-play methods, additional constraints are added to the cost function of any inverse problem. Since one step of the resulting new problem is similar to Gaussian denoising problem, this step is attempted to be solved with any Gaussian denoiser iteratively. In this study, inspired by PnP research wave, a simple and vanilla convolutional neural network for Gaussian denoising problem is proposed for CS MRI problem. In the experiments where convergence analysis of the proposed scheme is performed, we confirmed that our algorithm is successful for MR image reconstruction.
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ÖgeA new antenna design methodology based on performance analysis of MIMO and defining novel antenna parameters(Graduate School, 2024-05-08)The rapid growth of wireless technology has created a significant demand for the design of Multiple-Input Multiple-Output (MIMO) antennas for wireless devices. MIMO antennas play a crucial role in meeting the requirements of current and future wireless standards, as they can maximize data rates in wireless communication systems by utilizing multiple channels within the same bandwidth. However, designing MIMO antennas for compact devices presents considerable challenges. The limited space between antennas leads to increased coupling and high correlation, which can negatively impact their performance. To address these challenges, this thesis proposes a new antenna design methodology based on MIMO performance metrics and defining antenna parameters. Existing metrics for conventional antenna systems are insufficient for fully assessing MIMO antenna performance. This methodology provides a systematic approach to optimize antenna configurations, mitigate mutual coupling, and achieve desired performance characteristics, paving the way for enhanced system capacity. The thesis introduces a novel methodology for designing MIMO antennas that relies on crucial performance metrics and defining parameters. These parameters include factors such as antenna spacing, slot dimensions, strip placements, and parasitic element sizes, which are important for meeting the requirements of modern wireless standards within the LTE and sub-6 GHz 5G bands. The research presents five distinct MIMO antenna designs, each optimized for specific requirements and validated through simulations and experimental measurements. Firstly, the dual-band Vivaldi-shaped MIMO antenna covers the 5G NR bands n78 and n79, boasting gains of over 7.63 dBi and 8.5 dBi respectively, while maintaining mutual coupling below -30 dB. Secondly, the concentric octagonal-shaped MIMO antenna is designed for 5G UE applications in the n38 band, achieving a gain of over 5 dBi and mutual coupling below -25 dB. Thirdly, the compact quad-element MIMO antenna is designed for LTE/Wi-Fi applications, exhibiting high isolation exceeding 17 dB and a channel capacity loss lower than 0.6 b/s/Hz. Fourthly, the wideband MIMO antenna is a single-element design with quad-ports, operating in the 2.1/2.3/2.6 GHz and 2.4 GHz bands. It offers an operating bandwidth of 2-3.0 GHz, reflection coefficients below -10 dB, isolation under -25 dB using synthesized pi-networks TL-based decoupling network, and a diversity gain of approximately 10 dB. Finally, a quad-element MIMO antenna utilizing a modified Apollony fractal, designed for 5G wireless communications, achieves S11 below or equal to -10 dB within the impedance bandwidth, with low mutual coupling below -20 dB. The thesis explores various decoupling strategies to mitigate mutual coupling and enhance antenna performance. These strategies include antenna placement and orientation, parasitic elements, neutralization, and synthesized Pi-networks TL-based decoupling network topology. Each design is thoroughly evaluated through simulations and experimental measurements, with performance metrics including S-parameters, envelope correlation coefficient (ECC), channel capacity, total active reflection coefficient (TARC), and diversity gain. The research demonstrates the feasibility and effectiveness of the proposed methodology for designing compact MIMO antennas that offer improved performance metrics, making them well-suited for use in 5G and beyond wireless communication systems.
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ÖgeResource allocation mechanisms for end-to-end delay optimization of 5G URLLC services(Graduate School, 2024-09-16)5G and beyond networks aim to satisfy the challenging requirements of a variety of vertical services and domains such as automatic driving, health services, augmented/virtual reality and gaming, and streaming in addition to traditional mobile communication services and applications. Each service has its own specific QoS requirements such that enhanced mobile broadband (eMBB) services aim to provide higher data transmission rates and higher spectral efficiency for bandwidth-hungry applications and high-volume data processing services while ultra-reliable low-latency communications (URLLC) services are optimized for critical applications with stringent delay and reliability requirements. Allocating resources to higher priority URLLC services for its stringent delay and reliability requirements needs to be done carefully without jeopardizing the throughput performance of eMBB services. Simultaneously meeting the requirements of distinct services is a challenging task and requires innovative and intelligent resource allocation solutions. Network Slicing (NS) and Multi-Access Edge Computing (MEC) have emerged as promising enablers that can be utilized to manage network resources to satisfy application-specific requirements. NS enables the formation of end-to-end logical networks over an underlying infrastructure, spanning multiple network segments. This allows for cooperative allocation of resources across access, transport, and core components. NS simplifies the management of network resources, making it easier to provide tailored services for various applications, such as eMBB and URLLC. MEC serves as a crucial building block in mobile networks by facilitating the execution of computation tasks offloaded through wireless links. MEC enhances efficiency by bringing computational capabilities to the network's edge, resulting in faster response times and enhanced user experiences. Resource management for RAN slicing is a highly challenging task due to limited radio resources including frequency and power, stringent service requirements, dynamic wireless channel conditions, and random traffic arrivals. Effective management of communication and computation resources in RAN is needed to optimize resource utilization and satisfy service requirements. Innovative and intelligent resource allocation solutions utilizing artificial intelligence (AI) and machine learning (ML) will be key enablers to jointly optimize the performance of multiple services by providing real-time adaptation of the edge with respect to time-varying network conditions. Reinforcement Learning (RL) has become a popular tool for intelligent resource management in RAN. RL is an interdisciplinary area of machine learning in which an agent is trained to make a sequence of decisions by interacting with the environment whereby the agent chooses an action from a set of possible actions after observing the current system state and then receives a reward. Upon executing the action, the environment transitions to a new state. The primary goal of RL algorithms is to optimize action selection to maximize cumulative long-term rewards. In this study, we employ deep Q-learning (DQL) as our RL method. DQL employs a deep neural network (DNN) to achieve an action selection policy which maximize the expected cumulative reward. This sub-discipline of RL is referred to as deep reinforcement learning (DRL). In this thesis, we propose DRL-based resource management mechanisms for RAN slicing where URLLC and eMBB slices co-exist. The proposed resource distribution mechanisms aim to maximize the throughput for eMBB traffic while simultaneously satisfying the delay requirement of URLLC traffic. DRL-based resource allocation design includes hierarchically placed layers. The main DRL agent located at the upper layer performs inter-slice resource distribution while the URLLC and eMBB sub-agents are responsible for intra-slice resource allocation. In addition, we presented methods to reduce state and action spaces for computationally efficient DRL training and scalable design. Furthermore, we proposed methods to make the agents independent from each other in the hierarchical resource allocation design. In the first study, DRL-based resource allocation design is utilized for downlink transmission in RAN. In this study, the communication resource under consideration is the Resource Block (RB). In the second study, the resource allocation system is designed to jointly allocate communication (resource block) and computation (CPU cycle frequency) resources available in the base station and MEC server for task offloading operations. In both studies, packet delay analysis is presented by including queuing, transmission and computation delays. Moreover, resource allocation problems are formulated by including the QoS requirements of the URLLC and eMBB slices. The experiments are performed using various traffic scenarios and numerical results are compared with different baseline algorithms.
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ÖgeCompressive sensing of cyclostationary propeller noise(Graduate School, 2023-09-12)This dissertation is the combination of three manuscripts -either published in or submitted to journals- on compressive sensing of propeller noise for detection, identification and localization of water crafts. Propeller noise, as a result of rotating blades, is broadband and radiates through water dominating underwater acoustic noise spectrum especially when cavitation develops. Propeller cavitation yields cyclostationary noise which can be modeled by amplitude modulation, i.e., the envelope-carrier product. The envelope consists of the so-called propeller tonals representing propeller characteristics which is used to identify water crafts whereas the carrier is a stationary broadband process. Sampling for propeller noise processing yields large data sizes due to Nyquist rate and multiple sensor deployment. A compressive sensing scheme is proposed for efficient sampling of second-order cyclostationary propeller noise since the spectral correlation function of the amplitude modulation model is sparse as shown in this thesis. A linear relationship between the compressive and Nyquist-rate cyclic modulation spectra is derived to utilize matrix representations for the proposed method. Cyclic modulation coherence is employed to demonstrate the effect of compressive sensing in terms of statistical detection. Recovery and detection performances of sparse approximation algorithms based on greedy pursuits are compared. Results obtained with synthetic and real data show that compression is achievable without lowering the detection performance. Main challenges are weak modulation, low signal-to-noise ratio and nonstationarity of the additive ambient noise, all of which reduce the sparsity level causing degraded recovery and detection performance. Higher-order cyclostationary statistics is introduced to characterize propeller noise due to its non-Gaussian nature. The third-order cyclic cumulant spectrum, also known as the cyclic bispectrum, is derived and its sparsity is demonstrated for the amplitude modulated propeller noise model. Cyclic modulation bispectrum is proposed for feasible approximation of the cyclic bispectrum based solely on the discrete Fourier transform. Additionally, compressive sensing of the cyclic modulation bispectrum is suggested. Numerical results are presented for acquisition of the propeller tonals using real-world underwater acoustic data. Tonals estimated by third-order cyclic modulation bicoherence are more notable than the ones obtained by second-order cyclic modulation coherence due to latter's higher noise floor. Sparse recovery results show that frequencies of the prominent tonals can be obtained with sampling significantly below the Nyquist rate. The accurate estimation of tonal magnitudes, on the other hand, is challenging even with large number of compressive samples. Compressive sensing can be extended to solve underdetermined system of equations which appears in direction-of-arrival estimation with uniform linear arrays. An estimator is proposed based on the compressive beamformer for cyclostationary propeller noise. Its asymptotic bias is derived, which is inherited from the conventional beamformer when there are multiple sources. Squared asymptotic bias and the finite-sample variance, also derived explicitly, constitute the mean-squared error. Spectral averaging is suggested to mitigate this error by decreasing the adverse effect of the spatial Dirichlet kernel. For low signal-to-noise ratios, averaging enables the proposed estimator to outperform the methods that assume stationarity. This is achieved even under weak cyclostationarity, numerous closely-spaced sources and few sensors. The proposed methods are not only suitable for compressive sensing of propeller cavitation noise but also for general class of cyclostationary signals. Relevant research areas include but are not limited to communication, radar, acoustics and mechanical systems with applications such as spectrum sensing, modulation recognition, time difference of arrival estimation, time-frequency distributions, compressive detection and rolling element bearing fault diagnosis.