LEE- Telekomünikasyon Mühendisliği-Doktora

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  • Öge
    New edge computing offloading methods for next generation wireless networks
    (Graduate School, 2023-07-17) Atan, Beste ; Durak Ata, Lütfiye ; 504142307 ; Telecommunications Engineering
    The increasing number of mobile applications and massive deployment of connected IoT devices cause computation load due to intensive task requests coming from devices. Computing these tasks by fulfilling the latency requirements of the features or applications are one of the big argument in the next generation of networks. Although mobile or IoT devices provide us with various kinds of functionalities such as entertainment, social networking, or smart home or transportation, but they are not sufficient to process huge data thyself in an effective way due to their storage, computation, and energy limitations. Besides, traditional cloud computing systems that are currently used in the networks are deployed with high distant from the user environments. Therefore, they are not appropriate to provide low-latency required services. One of the promising methods to address the aforementioned problems is offloading the task computing process to edge servers that are in close proximity to the users. This solution is defined by The European Telecommunications Standards Institute (ETSI) and called multi-access edge computing (MEC). According to the task execution requirements, MEC offers more storage and compute power while meeting stringent performance criteria like low latency, low energy consumption, or increased bandwidth. Even if edge computing is a key solution, the task execution decision process between devices and edge servers is a complex decision problem that conventional optimization algorithms struggle to solve to meet the execution requirements. In recent years, the development of learning methods in artificial intelligence (AI) technology helps to create new methods for resolving these kinds of challenging problems. Additionally, both academies and industries have a significant interest to integrate machine learning (ML) and deep learning (DL) methods into next-generation wireless networks. By integrating AI algorithms into edge network environments, it is possible to accelerate the task execution decision-making process and answer the latency-sensitive task execution demands of mobile and IoT devices. In this thesis, AI-based learning methods are mainly considered for task offloading strategy and we propose an intelligent task execution decision framework to accelerate the edge computation offloading process. In this framework, we take into account the capacity of edge servers, channel conditions, delay and energy consumption of devices, and mobility of users. The system model utilized in this study is built to be feasible for serving a variety of smart connected devices with latency-sensitive applications such as online gaming by consoles, virtual reality (VR) or augmented reality (AR)-based applications, live video streams from unmanned aerial vehicles (UAVs), connected car or smartphone applications. In the first section, task offloading decision methods are investigated for mobile networks by utilizing machine learning (ML) algorithms. In the second section, the study is enhanced to serve multi-device environments for mobile and IoT networks and the AI-empowered task classification framework is designed to respond delay sensitive task execution requests. Comprehensive performance results demonstrate that the proposed AI-empowered framework is substantially fast and precise in the decision-making of the edge computation offloading process while maintaining the quality of user experience compared to conventional optimization methods. We believe that the proposed AI-enabled task classification framework could provide prominent solutions for new applications by running at the network edge.
  • Öge
    Antenlerin hızlı ve doğru tasarımı için esnek hesaplamaya dayalı sayısal karma yöntemler
    (Lisansüstü Eğitim Enstitüsü, 2023-07-11) Yiğit, Mahmud Esad ; Günel, Tayfun M. ; 504142305 ; Telekomünikasyon Mühendisliği
    Mikrodalga devre ve sistemlerin karmaşıklığındaki artıştan dolayı basitleştirilmiş teorik modeller, yapının performansının doğru şekilde tespit edilmesinde artık yeterli gelmemektedir. Bu yüzden tasarım güvenilirliğini garantiye almak için tam-dalga elektromagnetik analizin kullanımı zaruri olmuştur. Bunun yanında simülasyon (benzetim) yazılımları ve bilgisayar donanımlarındaki gelişmelere rağmen nümerik optimizasyon algoritmaları hala yüksek hesaplama yükü ve maliyetine sahiptir. Yüksek kalitede modellemeler mikrodalga sistemlerinin daha iyi ve verimli şekilde tasarımını mümkün kılmıştır. Anten analizi ve sentezi için de bu modellemeleri kullanan elektromagnetik tabanlı bazı simülasyon yazılımları geliştirilmiştir. Böylece daha büyük ve karmaşık sistemler tasarlanabilmektedir. Fakat elektromagnetik simülasyonların hesaplama yükleri çok fazladır. Tasarım parametreleri arttıkça yazılımlar ile yapılan analizlerde işlem yükü ve harcanan zaman artmaktadır. Antenlerde istenilen cevabı almak için fiziksel yapısındaki değişiklikler için optimizasyon işleminin uygulanması gerekir. Tasarımcının bilgi ve tecrübesi yeterli olsa da deneme yanılma yöntemiyle tekrarlı olarak yazılımların çalıştırılması tasarım süresini uzatmaktadır. Tasarım karmaşıklığı arttıkça daha yüksek doğrulukta ve hızda tasarım yöntemleri gerekmektedir. Bu çalışmada, anten tasarımı için hesaplama süresini kısaltmak ve sonuçları yüksek doğrulukla elde etmek için yeni karma yöntemlerin geliştirilerek kullanılması amaçlanmıştır. Hesaplama süresini kısaltmak ve istenilen performansa en yakın performansı veren anten yapısını tasarlayabilmek için de Esnek Hesaplamaya (EH) dayalı sayısal karma yöntemlerin geliştirilmesi ve daha sonra anten tasarımda kullanılması hedeflenmiştir. "Esnek Hesaplama", 1994 yılında Lotfi A. Zadeh tarafından ortaya atılmıştır. EH, kesin olmayan ve belirsiz koşullar içeren problemler için gözlenebilir, güvenilir ve düşük maliyetli çözümler sağlayan bir grup yöntemi ifade eder. EH tekniklerinde elde edilen sonuçlar matematiksel olarak kesin değildir ve yaklaşık optimum sonuçlar elde edilir. EH, çeşitli hesaplama tekniklerinin toplamıdır. Bazı EH teknikleri ve uygulamaları arasında bulanık mantık, evrimsel algoritmalar, Yapay Sinir Ağları (YSA) ve Destek Vektör Makineleri (DVM) gibi yöntemler yer alır. Bu tez çalışmasında literatürde verilen ve sık kullanılan evrimsel algoritmaların yanında yeni karma optimizasyon algoritmaları da geliştirilmiş ve anten tasarımlarında kullanılmıştır. Bu tezin birinci bölümünde tezin amacı ortaya koyulmuş, bu konuda literatürde yapılan çalışmalara kısaca değinilmiştir. İkinci bölümde ise anten tasarımı için gereken önemli parametreler ve tanımları verilmiştir. Ayrıca literatürde yaygın olarak kullanılan bazı elektromagnetik simülasyon yazılımları anlatılmıştır. Üçüncü bölümde tez çalışmasında kullanılan esnek hesaplama yöntemlerinden bahsedilmiştir. YSA, Destek Vektör Regresyonu (DVR) yöntemlerinin yanında Genetik Algoritma (GA), Parçacık Sürü Optimizasyonu (PSO) ve Taguchi Algortiması (TA) gibi literatürde verilen bazı evrimsel algoritmaların akış diyagramları verilerek ayrıntılı olarak anlatılmıştır. Daha sonra bu tez çalışmasında yeni geliştirilen Konveks-Genetik-Taguchi Algoritması (KGTA), Hibrit (Karma) Parçacık Sürü Taguchi Optimizasyonu (HPSTO) ve Taguchi-Genetik-Parçacık Sürü Optimizasyonu (TAGAPSO)'nun detayları açıklanmış ve akış şemaları verilmiştir. Yeni geliştirilen bu karma yöntemlerin performansları literatürde verilen bazı test fonksiyonları yardımıyla incelenmiştir. Test sonuçları, yeni geliştirilen karma yöntemlerin içerdikleri yöntemlere göre üstünlüklerini göstermiştir. Dördüncü bölümde ise tez çalışmasında geliştirilen EH tabanlı karma yöntemler ile literatürde verilen bazı yöntemler birlikte veya ayrı ayrı kullanılarak özgün mikroşerit antenlerin, konformal antenlerin ve anten dizilerinin tasarımları yapılmıştır. Tasarım işlemleri için HFSS ve MATLAB yazılımları kullanılmıştır. HFSS ve MATLAB ile elde edilen simülasyon ve optimizasyon sonuçlarıyla karşılaştırmalar yapılmıştır. Yapılan karşılaştırmalar sonucunda önerilen karma yöntemlerin üstünlükleri ortaya konulmuştur. Ayrıca tasarımı yapılan antenlerden bir tanesi gerçeklenerek teorik yaklaşımla elde edilen sonuçların doğruluğu gösterilmiştir. İlk olarak bir silindirik dikdörtgen mikroşerit konformal antenin tasarımı GA tabanlı YSA yaklaşımı, GA tabanlı DVR yaklaşımı ve HFSS'in optimizasyon aracı kullanılarak yapılmıştır. GA tabanlı YSA, GA tabanlı DVR ve HFSS optimizasyonun sonuçları gereken süre ve doğruluk yönünden karşılaştırılmış ve DVR'nin üstünlüğü görülmüştür. Daha sonra silindirik dikdörtgen halka mikroşerit konformal anten tasarımında PSO tabanlı YSA ve DVR yaklaşımları kullanılmıştır. DVR'de kernel fonksiyonu olarak Radyal Temelli Fonksiyon (RTF)'nin yanında dalgacık kernel fonksiyonları da kullanılmıştır. Bilindiği kadarıyla dalgacık kernel fonksiyonları DVR ile anten tasarımında ilk defa kullanılmıştır. Anten tasarımında eğitim süresi ve doğruluk bakımından dalgacık kernel fonksiyonlarının RTF ve YSA'ya olan üstünlükleri gösterilmiştir. Anten tasarımı PSO tabanlı yaklaşımla yapılırken dalgacık kernelleri kulanıldığına RTF kerneline göre daha az iterasyon ve zaman gerekmiş ayrıca daha doğru sonuçlar elde edilmiştir. Bir sonraki çalışmada, WLAN'ın iki frekans bandında da çalışacak çift yarıklı bir mikroşerit anten tasarımı DVR yaklaşımı ile yapılmıştır. Bilindiği kadarıyla iki bantta çalışacak antenin DVR ile tasarımı ilk defa yapılmıştır. Çift yarıklı dikdörtgen mikroşerit antenin oluşturulan DVR modeli istenen rezonans frekansını veren yarık boyutu değerlerini bulmak için kullanılmıştır. DVR modeli tersine bir şekilde çalıştırıldığı için ilave bir optimizasyon yöntemi kullanılması gerekmemiştir. Aynı yama boyutları ve taban malzemesine sahip analitik fonksiyonlar kullanılarak tasarlanan klasik dikdörtgen mikroşerit anten (DMA)'e göre antenin performans çıktılarındaki iyileşmeler gösterilmiştir. Diğer bir çalışmada, DVR modeli yaklaşımıyla anten yamasına birden fazla yarık ve yay-kesiği uygulanarak, istenen rezonans frekansları ve kazanç değerlerine sahip üç bantlı özgün bir mikroşerit anten tasarlanmıştır. Bilindiği kadarıyla üç bantta çalışacak anten ilk defa DVR yaklaşımı ile tasarlanarak gerçeklenmiştir. DVR yaklaşımı ile tasarlanan antenin HFSS simülasyon sonuçları ve gerçeklenen antenin ölçüm sonuçları verilmiştir. Gerçeklenen antenin performansı literatürde verilen üçlü bant özelliğine sahip antenler ile karşılaştırılmıştır. Ayrıca, karşılaştırma amacıyla HFSS'deki optimizasyon aracı kullanılarak anten yeniden tasarlanmıştır. DVR model yaklaşımı ve HFSS optimizasyonu ile tasarım, sonuçlarının doğruluğu ve tasarım süresi bakımından karşılaştırılmıştır. Daha sonra doğrusal anten dizilerinin YDS ve YGDG'lerini istenilen değerlerde elde etmek için yeni geliştirilen KGTA, HPSTO ve TAGAPSO karma optimizasyon algoritmaları kullanılmıştır. Yeni geliştirilen karma algoritmalar ile elde edilen sonuçlar içerdikleri diğer algoritmalarla elde edilen sonuçlarla karşılaştırılarak üstünlükleri ortaya koyulmuştur. Son bölümde ise tez çalışması genel olarak özetlenerek elde edilen sonuçlar kısaca açıklanmış ve gelecekteki çalışmalar için öneriler sunulmuştur.
  • Öge
    New deep learning based approaches for land cover classificationin satellite images
    (Graduate School, 2025-03-10) Awad, Bahaa ; Erer, Işın ; 504182301 ; Telecommunications Engineering
    This dissertation provides an in-depth examination of strategies for improving agricultural monitoring through remote sensing. It focuses on three main contributions: the development of FAUNet for delineating parcel boundaries, a novel technique that combines the Segment Anything Model (SAM) with principal component analysis (PCA) to refine segmentation, and the use of thermal time modeling to enhance crop classification across different climates. Parcel boundary delineation serves as a crucial step in agricultural monitoring, ensuring precise segmentation of land parcels for applications such as yield forecasting and land-use planning. To address limitations in existing models, this study presents FAUNet—an innovative dual-headed U-Net specifically tailored for boundary detection in agricultural imagery. FAUNet employs a high-frequency attention module (based on high-pass filters) and a dual-path design that predicts both edge and extent masks. When compared to leading models like U-Net, ResUNet-a, SEANet, and BsiNet, FAUNet delivers the highest object-level F1 score (0.7734) and exhibits notably low over-segmentation (0.0341) and under-segmentation (0.1390) rates. Building on these insights, the dissertation introduces a new method to enhance segmentation by coupling SAM—a foundational segmentation model originally trained on diverse natural images—with PCA. Since SAM's training data do not include specialized remote sensing imagery, its performance in this domain can be limited. To address this issue, SAM's high-dimensional embeddings are first extracted, then reduced with PCA, followed by guided filtering to refine the inputs. This iterative feedback loop helps SAM generate more precise boundary delineations, ultimately improving segmentation results in challenging remote sensing scenarios. The thesis then turns its attention to the challenge of generalizing crop classification models in regions with varying climates. Standard machine learning models (e.g., Random Forest, Gradient Boosting, XGBoost, SVM, and MLP) often encounter difficulties when facing the temporal shifts driven by different local growing conditions. To mitigate this, thermal time modeling based on Growing Degree Days (GDD) is introduced. By aligning crop phenological stages and smoothing out timing discrepancies, GDD helps these models adapt to spatial variability more effectively. Experiments on datasets from Turkish regions with diverse climates show that incorporating GDD boosts classification performance, allowing models to generalize more reliably across geographically distinct environments. Overall, this dissertation tackles significant obstacles in agricultural remote sensing, ranging from accurate parcel boundary detection to robust crop classification under complex environmental conditions. The proposed FAUNet framework streamlines boundary delineation, the SAM modification allows it to perform better in boundary delineation, and thermal time modeling underscores how classification can be adapted for real world agricultural scenarios.
  • Öge
    Index modulation based designs, error performance and physical layer security analyses for unmanned aerial vehicle networks
    (Graduate School, 2024-07-22) Büyükşar, Ayşe Betül ; Altunbaş, İbrahim ; 504172302 ; Telecommunication Engineering
    Current 5G networks will not be able to meet emerging communications demands. As a result, research has begun on 6G wireless communication networks, which are expected to be deployed after 2030. 6G wireless communication networks will further improve mobile broadband, extend coverage and enable networks to include more and more smart devices. Reconfigurable front-ends for dynamic spectrum access, the Internet of Space Things enabled by CubeSats and Unmanned Aerial Vehicles (UAVs), cell-free massive multiple-input multiple-output (MIMO) communication networks, and intelligent communication environments that enable a wireless propagation environment with active signal transmission and reception are key technology advances to meet the requirements of 6G networks. Therefore, 6G will introduce new technical requirements and performance metrics driven by new application needs. 6G networks using terahertz and optical radio bands could reach $1-10$ terabytes per second. Moreover, high-frequencies can provide data rates that can reach gigabytes per second for user experience. Spectrum efficiency can be increased by a factor of $3-5$, while energy efficiency can be increased by a factor of about $10$ through the use of artificial intelligence for better network management compared to 5G. Other key performance indicators, such as cost-effectiveness, security capacity, coverage, intelligence levels, etc., should also be established to provide a more complete assessment of 6G networks. To provide global coverage, 6G wireless communication networks will expand from terrestrial communication networks in 1G-5G to integrated space-air-ground-sea networks, including satellites, UAVs, terrestrial networks, and marine communications. Over the past few years, a wide range of applications for UAVs has been established due to the advantages of their flexible design, rapid deployment, and low cost. A UAV can be used as aerial base stations (BS), user equipment (UE), or relay terminal in the 6G network because of their flexible design. UAVs can also be used in satellite networks which are another potential communication platform for 6G. In spite of the significant progress made in UAV technology, there are still several challenges. To enable UAV-based communication systems, extensive research is needed to accurately model the channel as UAV channels are unique due to their 3D deployment, high mobility, spatial and temporal instability, and aircraft shadowing. In addition to channel modeling challenges, UAV-based communications face several challenges, including security and regulatory issues, limited battery life, and seamless integration with existing networks. Index modulation (IM) can be considered as a potential technique to increase the spectral efficiency of UAV-based communications. IM uses information about the main building blocks of the wireless communication network to increase spectral efficiency and due to its advantages it has attracted considerable interest from the academia over the past decade. One of the common IM techniques, spatial modulation (SM), maps the information bits to the antenna indices. Similar to the concept of SM, distributed spatial modulation (DSM) allows the transmission of information bits using relay indices in a cooperative system. The DSM technique increases the aggregate throughput of the system and improves source reliability through distributed diversity. Another common IM technique, media-based modulation (MBM), embeds information in the selection of a particular transmission channel from a variety of channel states created by integrating parasitic elements such as radio frequency (RF) mirrors and PIN diodes in a reconfigurable antenna (RA). Similar to the external parasitic elements in MBMs, intelligent reflective elements in reconfigurable intelligent surfaces (RIS) enrich the propagation environment and perform proper phase shifts to modify the channel. This improves overall signal-to-noise ratio (SNR) quality by utilizing low-cost PIN diodes or varactors. Although MBM and RIS are based on similar structures, the MBM technique is designed to transmit additional information bits, while RIS increases the overall system reliability. 6G communication networks are designed for full connectivity with high operational flexibility and autonomy. Despite these advantages, the heterogeneity of the 6G network with UAVs and satellites makes it more vulnerable to security threats. For this reason, physical layer security (PLS) can act as an additional layer of security to enhance the trustworthiness of the radio access network. Traditional PLS solutions, like using active relays or friendly jammers (FJs) which use artificial noise (AN) to provide security, can result in increased hardware costs and power consumption. In this thesis, first the UAV channels are investigated with measurements. Then, in order to meet the high reliability requirements of future generation networks, integrated UAV systems are considered and novelties with solid theoretical foundations are proposed using DSM, and MBM. After reliability analysis for UAV systems, security problems are considered and novel system designs with non-orthogonal multiple access (NOMA), SSI-based UAV relay selection, joint transmit-receive pattern selection (JTRPS), and RIS are analytically investigated. In the first part of this thesis, we have measured the air-to-ground (AtG) channel by exploiting its statistics in realistic outdoor channel conditions for the UAV. In this study, the path loss exponent is found with curve fitting and fading statistics are estimated using the maximum likelihood (ML) decision rule. Practical measurements showed that the AtG channel is likely to be modeled with Nakagami and Rician distributions. In the second part of this thesis, DSM technique, one of the IM techniques, is considered for both ground-to-ground (GtG) communication with UAV relays and UAV BS included AtG communication from error performance perspective due to the increased throughput advantage of DSM. By considering inherited characteristics of UAVs such as limited power, we proposed a cyclic redundancy check (CRC) aided UAV-relays. In this way erroneous UAV activation, error propagation and futile power consumption are prevented. Furthermore, DSM is generalized by using relay indices and modulated symbols for UAV BS to transmit information. As a continuation study of IM techniques for UAVs, MBM technique is realized by using RAs with mirror activation patterns (MAPs), which depend on the different on-off situations of RF mirrors. By this way the higher capacity gains can be achieved since the channel coefficients received from multiple paths are independent and identically distributed (i.i.d.). Therefore, a novel RA-embedded UAV relay-aided dual-hop communication system is proposed, combining SSI-based MAP selection at the first hop with the MBM technique at the second hop. As only one RF chain is required in this system, RA-embedded UAVs are cost-effective. In addition, SSI-based MAP selection improves spectral efficiency by eliminating a high data rate feedback channel carrying fast channel state information (CSI). For the purpose of simplifying the theoretical analysis and taking into account the standardization parameters, the AtG channel is modeled with a double Nakagami distribution. Theoretical bit error probability (BEP) analysis and asymptotic expressions are obtained and validated with simulation results. Besides the spectrum efficiency and high reliability {\color{Green}concerns}, PLS is also a significant concept for 6G networks. Especially, the intrinsic broadcast nature of UAVs and satellites makes them more susceptible to security threats. In particular, UAV eavesdroppers or UAV jammers have a physical channel advantage because of the high line-of-sight (LOS) probability with ground users. Motivating from this point, security threats are investigated for UAV networks. In the first part of the PLS analysis, NOMA based UAV BS aided terrestrial networks are investigated with secrecy analysis. In the second part of the PLS analysis, passive and active eavesdropper (PE/AE) {\color{Green}UAVs are} considered in space-air-ground-integrated network (SAGIN) that includes full-duplex (FD) UAV relays. Furthermore, the received SNR is increased with SSI-based relay selection, which improves the outage probability (OP). One of the PLS improving methods, FJ, is deployed in SAGIN by selecting from the FD-UAV relays. The proposed system operates in one transmission slot, unlike its half-duplex (HD) counterparts. Transmission secrecy outage probability (TSOP) expressions are derived to comprehensively evaluate the reliability and security performance of the SAGIN. In the third part of the PLS analysis, RIS, {\color{Green}which} favorably adapts the propagation environment by using low-cost reflective elements (REs), is considered for aerial communication in the existence of UAV eavesdropper to enhance security performance. To improve the received SNR both JTRPS and ideal phase shifting at RIS are proposed. Moreover, capacity-based secrecy outage probability (SOP) and TSOP expressions are derived and theoretical results are validated by simulations. In summary, this thesis presents novel UAV-included communication systems for future-generation networks by considering realistic channel models and channel parameters in standardization studies. Through this process, we deployed DSM, relay and pattern selection, MBM, NOMA techniques to UAV-based systems. Moreover, we initially investigated BEP, symbol error probability (SEP) to evaluate the reliability of the UAV systems. To investigate the secrecy performance of UAV systems, SOP, and TSOP expressions are studied with detailed comprehensive analysis.
  • Öge
    A new approach to satellite communication: Harnessing the power of reconfigurable intelligent surfaces
    (Graduate School, 2024-01-22) Tekbıyık, Kürşat ; Kurt Karabulut, Güneş ; 504192305 ; Telecommunications Engineering
    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.