Future wireless networks are envisioned to support a wide range of novel use cases, and connect a massive number of people and devices in an energy efficient way. Several key enabling technologies were considered to support this vision including Internet of Things (IoT) networks, aerial and stratospheric platforms, and reconfigurable intelligent surfaces (RIS). In this dissertation, we study different problems related to the integration between these technologies. First, we propose a cost-effective framework for data collection from IoT sensors using multiple unmanned aerial vehicles (UAVs). This is achieved by effcient clustering of the sensors and optimized deployment of cluster heads (CHs). Then, the number of deployed UAVs and their trajectories will be optimized to minimize the data collection flight time. The impacts of the trajectory approach, environment type, and UAVs' altitude as well as the fairness of UAVs trajectories on the data collection process are investigated. Given that IoT nodes might have different priorities and time deadlines, and respecting the limited battery capacity of UAVs, we enhance the data collection framework to account for these practical constraints. First, an algorithm for finding the minimal number of CHs and their best locations is proposed. Then, the minimal number of UAVs and their trajectories are obtained by solving the associated capacitated vehicle routing problem. The results investigate the impacts of the selected trajectory approach, the battery capacity and time deadlines on the consumed energy, number of visited CHs, and number of deployed UAVs. Next, given the energy issue on aerial platforms, we present our vision for integrating RIS in aerial and stratospheric platforms to provide energy-efficient communications. We propose a control architecture for such integration, discuss its benefits and identify potential use cases and associated research challenges. Then, to substantiate our vision, we study the link budget of RIS-assisted communications under the specular and the scattering reflection paradigms. Specifically, we analyze the characteristics of RIS-equipped stratospheric and aerial platforms and compare their communication performance with that of RIS-assisted terrestrial networks, using standardized channel models. In addition, we derive the optimal aerial platforms placements under both reflection paradigms. The obtained results provide important insights for the design of RIS-assisted communications. For instance, given that a HAPS has a large RIS surface, it provides superior link budget performance in most studied scenarios. In contrast, the limited RIS area on UAVs and the large propagation loss in low Earth orbit (LEO) satellite communications make them unfavorable candidates for supporting terrestrial users. Then, motivated by the demonstrated potential of HAPS equipped with RIS (HAPS-RIS), we propose a solution to support the stranded users in terrestrial networks through a dedicated control station (CS) and HAPS-RIS. We refer to this approach as "beyond-cell" communications. We demonstrate that this approach works in tandem with legacy terrestrial networks to support uncovered or unserved users. Optimal transmit power and RIS unit assignment strategies for the users based on different network objectives are introduced. Furthermore, to increase the percentage of admitted users in an efficient manner, a novel resource-efficient optimization problem is formulated that maximizes the number of connected UEs, while minimizing the total power consumed by the CS and RIS. Since the resulting problem is a mixed-integer nonlinear program (MINLP), a low-complexity two-stage algorithm is developed. Finally, given the different applications and various options of HAPS payload, we envision the use of a multi-mode HAPS that can adaptively switch between different modes so as to reduce energy consumption and extend the HAPS loitering time. These modes comprise a HAPS super macro base station (HAPS-SMBS) mode for enhanced computing, caching, and communication services, a HAPS relay station (HAPS-RS) mode for active communication, and a HAPSRIS mode for passive communication. This multi-mode HAPS ensures that operations rely mostly on the passive communication payload while switching to an energy-greedy active mode only when necessary. We illustrate the envisioned multi-mode HAPS, and discuss its benefits and challenges. Then, we validate the multi-mode efficiency through a case study. At the end of the dissertation, several future research directions are proposed including hybrid orthogonal and non-orthogonal multiple access (OMA/NOMA) beyond-cell communications assisted by HAPS-RIS, configuration of RIS units on stratospheric platforms, energy management for HAPS-RIS, and supporting aerial users through terrestrial RIS.

Next-generation wireless technologies are being actively researched to meet the growing demands for higher data rates, massive connectivity, enhanced reliability, and extended coverage. Recently, reconfigurable intelligent surfaces (RISs) and extremely large antenna arrays (ELAAs) have garnered significant attention as new physical-layer transmission technologies capable of achieving unprecedented spectral and energy efficiency gains. Consequently, RIS and ELAA are considered as promising key enabling technologies for the sixth-generation (6G) and future wireless standards.This dissertation investigates RIS and simultaneously transmitting and reflecting (STAR)-RIS assisted wireless communications, emphasizing design, optimization, and analysis across various practical settings. It presents wireless channel modelling techniques, system design aspects, fundamental performance limits/metrics, including outage probability, average achievable rate, average symbol error rate (SER), diversity order, computational complexity, and algorithmic foundations. This doctoral research also develops algorithms for optimizing RIS/STAR-RIS phase shifts and transmit power allocation in multi-user massive multiple-input multiple-output (MIMO) systems. Moreover, this dissertation characterizes unique propagation characteristics of ELAAs, and thereby impacts of visibility regions (VRs) and spatial non-stationarity in extra-large (XL) RIS communication set-ups with XL-massive MIMO base stations (BS) are analyzed.The dissertation begins with a fundamental performance analysis of RIS-assisted systems operating over Nakagami-m fading channels. It quantifies optimal phase-shifts to maximize received signal-to-noise ration (SNR) and derives the probability distribution of the SNR. The findings include closed-form expressions for outage probability, average SER, and achievable rate, demonstrating that these metrics improve as the number of RIS reflective elements increases. The study also reveals that the achievable diversity order scales linearly with the number of passive RIS elements, resulting in significant diversity gains without additional radio-frequency (RF) chains. Further investigation into STAR-RIS systems with discrete phase-shifts highlights the performance under different protocols, such as energy splitting (ES), mode switching (MS), and time splitting (TS), considering both unicast and multicast transmissions. The analysis demonstrates that employing four-bit phase-shift quantization significantly narrows the performance gap between discrete and continuous phase-shifts. Additionally, it is found that the average achievable rate and SER reach saturation levels at high transmit SNRs, influenced by power allocation coefficients at the transmitter. The dissertation also presents an achievable rate analysis and RIS phase-shift optimization for multi-cell RIS-aided massive MIMO, with for imperfect channel state information (CSI), co-channel interference, and spatially correlated fading. A statistical CSI-based transmit power allocation algorithm is proposed, reducing channel estimation overhead and ensuring user fairness. In exploring STAR-RIS aided multi-user massive MIMO systems, statistical CSI-based STAR-RIS phase-shift and transmit power optimization techniques are used to maximize composite channel gains and ensure fair user rates. The study quantifies the impacts of CSI imperfections, residual interference, and spatially correlated fading. Lastly, the effects of visibility regions in XL RIS setups are examined, deriving achievable user rates and employing phase-shift optimization to maximize user channel covariance. A max-min power allocation algorithm is utilized to address near-far user effects, ensuring system-wide user fairness. Overall, this dissertation provides comprehensive insights and advanced optimization techniques for enhancing RIS and STAR-RIS technologies in wireless communication systems.

Reconfigurable intelligent surfaces (RISs) are now considered among the key enabling technologies catering to the ever-increasing demand for traffic rate in the future fifth-generation beyond or even sixth-generation. RISs can be leveraged to transform the propagation environment into a smart space that can be programmable for the benefit of the communication application. Throughout this proposal, we study RIS-assisted systems from different perspectives to analyze and enhance the operation of such systems in different setups. In this context, we first analyze the performance of the RIS-assisted single-input single-output (SISO) system and make a fair comparison with the conventional relaying system. Then, we investigates the use of a RIS to aid point-to-point multi-data-stream multiple-input multiple-output (MIMO) wireless communications. With practical finite alphabet input, the reflecting elements at the RIS and the precoder at the transmitter are alternatively optimized to minimize the symbol error rate. Considering the same RIS-assisted MIMO system, We further explore the potential of RIS for acting as an active modulator and piggybacking its own information when helping the information transmission between the transmitter and the receiver at the same time. Furthermore, considering a RIS-assisted SISO system over the millimeter wave channel, we propose an appropriate design of the phase shifts of each element at the RIS so as to maximize the received signal power at the desired user, while nulling the received interference signal power at the undesired user. However, most of the works investigated the use of continuous phase shift designs, which cannot be implemented in practice. It motivates us to investigate the control of the phases shifts under the assumption that they belong to a finite discrete set. As the above-mentioned performance analysis and optimization of RIS-assisted system requires the channel state information, we thus address the channel estimation problem for a point-to-point SISO system and a point-to-point multiple-input single-output system, respectively. Finally, we highlight some possible future research directions to be considered for the thesis.

The rapidly increasing demands for high data-rate applications and the growth of wireless devices connected to the internet overcrowded the radio frequency spectrum. This necessitates researchers to examine higher frequencies for wireless communication. Recently, visible light communication (VLC) has received significant attention as a viable solution to complement the RF technologies, thanks to the abundant unregulated/unlicensed spectrum it occupies while utilizing the existing lighting infrastructure. However, due to the physical properties of light, the signal cannot penetrate through obstacles, and the VLC system heavily relies on the existence of a line-of-sight (LoS) link between VLC transmitters and receivers. Optical reconfigurable intelligent surfaces (RISs) are recently proposed with the ability to dynamically control the wireless channel, which offers opportunities to enhance the VLC system performance by exploiting the non-LoS components of the VLC link. In this thesis, we highlight the recent developments in optical RISs and the various reflection characteristics they provide for the incident optical beams. Then, we investigate RIS-assisted VLC systems for both indoor and outdoor setups. Firstly, in indoor VLC systems, we study multi-user RIS-assisted VLC systems while considering specular and diffuse reflecting RISs. As the channel gain varies significantly between users in VLC systems, a large gap in performance is observed between users. We aim to maximize the VLC system achievable sum rate while ensuring network fairness. We formulate multi-objective optimization problems for both specular and diffuse reflecting RISs and propose a solution utilizing low complex genetic algorithm (GA) and particle swarm optimization (PSO). We highlight the advantages provided by the proposed algorithms in terms of achievable sum rate and network fairness performance. In addition, we assess the link outage ratio for specular reflecting RISs and assess the gains provided by diffuse RISs while considering an environment with mobile users. Secondly, in the context of outdoor VLC systems, we provide an overview of outdoor RIS-assisted VLC systems. In particular, we highlight the benefits of optical RISs to mitigate LoS blockage and VLC transceivers misalignment. More specifically, we focus on RIS-assisted unmanned aerial vehicles (UAVs)-based VLC, RIS-assisted vehicular VLC, and RIS-assisted streetlight-based communication. In addition, we highlight the use of RISs to support VLC outdoor-to-indoor communications.

In recent years, metasurfaces have shown promising abilities to control and manipulate electromagnetic (EM) waves through modified surface boundary conditions. These surfaces are electrically thin and comprise an array of spatially varying sub-wavelength scattering elements (or meta-atoms). Metasurfaces can transform an incident EM wave into an arbitrarily tailored transmitted or reflected wavefront through carefully engineering each meta-atom. Recent developments in metasurfaces have opened exciting new opportunities in antenna design, sensing, and communications systems. In particular, reconfigurable metasurfaces - wherein meta-atoms are embedded with active components - lead to the development of low-cost, lightweight, and compact systems capable of producing programmable radiation patterns and jointly performing multi-function communications, and enable advanced sensors for next-generation platforms. This research introduces reconfigurable metasurfaces and their various applications in designing simplified communications systems, wherein the RF aperture and transceiver are integrated within the metasurface. Finally, we will present our recent work on reconfigurable metasurfaces control, metasurface-enabled direct signal modulation, and deep learning-based metasurface design. / Doctor of Philosophy / Metasurfaces are a promising new technology to enhance the capacity and coverage of wireless communication networks by dynamically reconfiguring the wireless propagation environment. These low-profile artificial electromagnetic surfaces, consisting of subwavelength resonant elements, are capable of tailoring electromagnetic waves controllably. In this dissertation, we control the transmission or reflection properties of the surface using digital codes by embedding tunable elements within each subwavelength element. Furthermore, metasurface antennas are a promising candidate for reducing the cost and hardware footprint of wireless sensor systems, such as radar or imaging. Using a digital microcontroller, we program the metasurface to steer the antenna beam in the direction of interest, modulate the radio wave, or change the polarization of an incoming signal. In addition to dynamic beamforming capabilities, we program the metasurface to reduce the scattering of an incoming signal, thereby reducing its perturbations on the radio environment. Still, the design of metasurfaces for specific applications remains complex and technically challenging. Lastly, we present innovative deep learning techniques to simplify metasurface design.

Cette thèse porte principalement sur la modélisation, l'évaluation des performances et l'optimisation au niveau système des réseaux cellulaires de nouvelle génération à l'aide de la géométrie stochastique. En plus, la technologie émergente des surfaces intelligentes reconfigurables (RISs) est étudiée pour l'application aux futurs réseaux sans fil. En particulier, reposant sur un modèle d’abstraction basé sur la loi de Poisson pour la distribution spatiale des nœuds et des points d’accès, cette thèse développe un ensemble de nouveaux cadres analytiques pour le calcul d’importantes métriques de performance, telles que la probabilité de couverture et l'efficacité spectrale potentielle, qui peuvent être utilisés pour l'analyse et l'optimisation au niveau système. Plus spécifiquement, une nouvelle méthodologie d'analyse pour l'analyse de réseaux cellulaires tridimensionnels est introduite et utilisée pour l'optimisation du système. Un nouveau problème d’allocation de ressources est formulé et résolu en combinant pour la première fois géométrie stochastique et programmation non linéaire mixte en nombres entiers. L'impact du déploiement de surfaces réfléchissantes intelligentes sur un réseau sans fil est quantifié à l'aide de processus ponctuels, et les avantages potentiels des RISs contre le relais sont étudiés à l'aide de simulations numériques. / The main focus of this thesis is on modeling, performance evaluation and system-level optimization of next-generation cellular networks by using stochastic geometry. In addition, the emerging technology of Reconfigurable Intelligent Surfaces (RISs) is investigated for application to future wireless networks. In particular, relying on a Poisson-based abstraction model for the spatial distribution of nodes and access points, this thesis develops a set of new analytical frameworks for the computation of important performance metrics, such as the coverage probability and potential spectral efficiency, which can be used for system-level analysis and optimization. More specifically, a new analytical methodology for the analysis of three-dimensional cellular networks is introduced and employed for system optimization. A novel resource allocation problem is formulated and solved by jointly combining for the first time stochastic geometry and mixed-integer non-linear programming. The impact of deploying intelligent reflecting surfaces throughout a wireless network is quantified with the aid of line point processes, and the potential benefits of RISs against relaying are investigated with the aid of numerical simulations.

Med framväxten av framtida mobilgenerationer bortom 5G, studeras nya teknologier för att tillgodose de förväntade kraven för framtida tjänster som Ultra-Reliable Low Latency Communications (URLLC) eller Virtual Reality. Bland dessa teknologier uppstår Reconfigurable Intelligent Surfaces (RIS) som en av de mest lovande på grund av deras förmåga att förbättra kanalen samtidigt som de bara ökar nätverkets energiförbrukning måttligt. Men flera utmaningar måste lösas innan de kan distribueras. I denna avhandling studerar vi strategier för att positionera RIS för att uppnå maximal SNR-täckning i en utomhusförökningsmiljö. Vår modell tar hänsyn till effekterna av skuggfading och siktlinje (LoS). En jämförelse mellan centraliserade och distribuerade distributioner övervägs också. Dessutom bedöms den nödvändiga storleken på RIS för att matcha täckningen av en liten cell. Resultaten visar att de bästa positionerna för att distribuera en RIS ligger nära de mobila terminalerna, i närheten av gränsen mellan täckta och utomtäckta områden. Man drar slutsatsen att en centraliserad distribution är bättre än en distribuerad, och en genomförbar storlek på RIS som matchar den lilla celltäckningen erhålls. / With the emergence of future mobile generations beyond 5G, novel technologies are studied to satisfy the envisioned requirements of future services such as Ultra-Reliable Low Latency Communications (URLLC) or Virtual Reality. Among these technologies, Reconfigurable Intelligent Surfaces (RIS) arise as one of the most promising due to their capabilities to improve the channel while only modestly increasing the network energy consumption. However, multiple challenges have to be addressed before they can be deployed. In this thesis, we study strategies for positioning the RIS to achieve maximum SNR coverage in an outdoor propagation environment. Our model takes into account the effects of shadow fading and line-of-sight (LoS). A comparison between centralized and distributed deployments is also considered. Additionally, the required size of RIS to match the coverage of a small cell is assessed. The results show that the best positions to deploy a RIS lie close to the mobile terminals, in the vicinity of the boundary between covered and out-of-coverage areas. It is concluded that a centralized deployment is better than a distributed one, and a feasible size of the RIS which matches the small cell coverage is obtained.

The concepts of a digital twin and extended reality have recently emerged, which require a massive amount of sensor data to be transmitted with low latency and high reliability. For low-latency communications, joint source-channel coding (JSCC) is an attractive method for error correction coding and compared to highly complex digital systems that are currently in use. I propose the use of complex-valued and quaternionic neural networks (QNN) to decode JSCC codes, where the complex-valued neural networks show a significant improvement over real-valued networks and the QNNs have an exceptionally high performance. Furthermore, I propose mapping encoded JSCC code words to the baseband of the frequency domain in order to enable time/frequency synchronization as well as to mitigate fading using robust estimation theory. Additionally, I perform robust statistical signal processing on the high-dimensional JSCC code showing significant noise immunity with drastic performance improvements at low signal-to-noise ratio (SNR) levels. The performance of the proposed JSCC codes is within 5 dB of the optimal performance theoretically achievable and outperforms the maximum likelihood decoder at low SNR while exhibiting the smallest possible latency. I designed a Bayesian minimum mean square error estimator for decoding high-dimensional JSCC codes achieving 99.96% accuracy. With the recent introduction of electromagnetic reconfigurable intelligent surfaces (RIS), a paradigm shift is currently taking place in the world of wireless communications. These new technologies have enabled the inclusion of the wireless channel as part of the optimization process. In order to decode polarization-space modulated RIS reflections, robust polarization state decoders are proposed using the Weiszfeld algorithm and an generalized Huber M-estimator. Additionally, QNNs are trained and evaluated for the recovery of the polarization state. Furthermore, I propose a novel 64-ary signal constellation based on scaled and shifted Eisenstein integers and generated using media-based modulation with a RIS. The waveform is received using an antenna array and decoded with complex-valued convolutional neural networks. I employ the circular cross-correlation function and a-priori knowledge of the phase angle distribution of the constellation to blindly resolve phase offsets between the transmitter and the receiver without the need for pilots or reference signals. Furthermore, the channel attenuation is determined using statistical methods exploiting that the constellation has a particular distribution of magnitudes. After resolving the phase and magnitude ambiguities, the noise power of the channel can also be estimated. Finally, I tune an Sq-estimator to robustly decode the Eisenstein waveform. / Doctor of Philosophy / This dissertation covers three novel wireless communications methods; analog coding, communications using the electromagnetic polarization and communications with a novel signal constellation. The concepts of a digital twin and extended reality have recently emerged, which require a massive amount of sensor data to be transmitted with low latency and high reliability. Contemporary digital communication systems are highly complex with high reliability at the expense of high latency. In order to reduce the complexity and hence latency, I propose to use an analog coding scheme that directly maps the sensor data to the wireless channel. Furthermore, I propose the use of neural networks for decoding at the receiver, hence using the name neural receiver. I employ various data types in the neural receivers hence leveraging the mathematical structure of the data in order to achieve exceptionally high performance. Another key contribution here is the mapping of the analog codes to the frequency domain enabling time and frequency synchronization. I also utilize robust estimation theory to significantly improve the performance and reliability of the coding scheme. With the recent introduction of electromagnetic reconfigurable intelligent surfaces (RIS), a paradigm shift is currently taking place in the world of wireless communications. These new technologies have enabled the inclusion of the wireless channel as part of the optimization process. Therefore, I propose to use the polarization state of the electromagnetic wave to convey information over the channel, where the polarization is determined using a RIS. As with the analog codes, I also extensively employ various methods of robust estimation to improve the performance of the recovery of the polarization at the receiver. Finally, I propose a novel communications signal constellation generated by a RIS that allows for equal probability of error at the receiver. Traditional communication systems utilize reference symbols for synchronization. In this work, I utilize statistical methods and the known distributions of the properties of the transmitted signal to synchronize without reference symbols. This is referred to as blind channel estimation. The reliability of the third communications method is enhanced using a state-of-the-art robust estimation method.