The emergence of 6G and beyond networks is set to enable a range of novel services such as personalized highly immersive experiences, holographic teleportation, and human-like intelligent robotic applications. Such applications require a set of stringent sensing, communication, control, and intelligence requirements that mandate a leap in the design, analysis, and optimization of today's wireless networks. First, from a wireless communication standpoint, future 6G applications necessitate extreme requirements in terms of bidirectional data rates, near-zero latency, synchronization, and jitter. Concurrently, such services also need a sensing functionality to track, localize, and sense their environment. Owing to its abundant bandwidth, one may naturally resort to terahertz (THz) frequency bands (0.1 − 10 THz) so as to provide significant wireless capacity gains and enable high-resolution environment sensing. Nonetheless, operating a wireless system at the THz band is constrained by a very uncertain channel which brings forth novel challenges. In essence, these channel limitations lead to unreliable intermittent links ergo the short communication range and the high susceptibility to blockage and molecular absorption. Second, given that emerging wireless services are "intelligence-centric", today's communication links must be transformed from a mere bit-pipe into a brain-like reasoning system. Towards this end, one can exploit the concept of semantic communications, a revolutionary paradigm that promises to transform radio nodes into intelligent agents that can extract the underlying meaning (semantics) or significance in a data stream. However, to date, there has been a lack in holistic, fundamental, and scalable frameworks for building next-generation semantic communication networks based on rigorous and well-defined technical foundations. Henceforth, to panoramically develop the fully-fledged theoretical foundations of future 6G applications and guarantee affluent corresponding experiences, this dissertation thoroughly investigates two thrusts. The first thrust focuses on developing the analytical foundations of THz systems with a focus on network design, performance analysis, and system optimization. First, a novel and holistic vision that articulates the unique role of THz in 6G systems is proposed. This vision exposes the solutions and milestones necessary to unleash THz's true potential in next-generation wireless systems. Then, given that extended reality (XR) will be a staple application of 6G systems, a novel risk and tail-based performance analysis is proposed to evaluate the instantaneous performance of THz bands for specific ultimate virtual reality (VR) services. Here, the results showcase that abundant bandwidth and the molecular absorption effect have only a secondary effect on the reliability compared to the availability of line-of-sight. More importantly, the results highlight that average metrics overlook extreme events and tend to provide false positive performance guarantees. To address the identified challenges of THz systems, a risk-oriented learning-based design that exploits reconfigurable intelligent surfaces (RISs) is proposed so as to optimize the instantaneous reliability. Furthermore, the analytical results are extended to investigate the uplink freshness of augmented reality (AR) services. Here, a novel ruin-based performance is conducted that scrutinizes the peak age of information (PAoI) during extreme events. Next, a novel joint sensing, communication, and artificial intelligence (AI) framework is developed to turn every THz communication link failure into a sensing opportunity, with application to digital world experiences with XR. This framework enables the use of the same waveform, spectrum, and hardware for both sensing and communication functionalities. Furthermore, this sensing input is intelligently processed via a novel joint imputation and forecasting system that is designed via non-autoregressive and transformed-based generative AI tools. This joint system enables fine-graining the sensing input to smaller time slots, predicting missing values, and fore- casting sensing and environmental information about future XR user behavior. Then, a novel joint quality of personal experience (QoPE)-centric and sensing-driven optimization is formulated and solved via deep hysteretic multi-agent reinforcement learning tools. Essentially, this dissertation establishes a solid foundation for the future deployment of THz frequencies in next-generation wireless networks through the proposal of a comprehensive set of principles that draw on the theories of tail and risk, joint sensing and communication designs, and novel AI frameworks. By adopting a multi-faceted approach, this work contributes significantly to the understanding and practical implementation of THz technology, paving the way for its integration into a wide range of applications that demand high reliability, resilience, and an immersive user experience. In the second thrust of this dissertation, the very first theoretical foundations of semantic communication and AI-native wireless networks are developed. In particular, a rigorous and holistic vision of an end-to-end semantic communication network that is founded on novel concepts from AI, causal reasoning, transfer learning, and minimum description length theory is proposed. Within this framework, the dissertation demonstrates that moving from data-driven intelligence towards reasoning-driven intelligence requires identifying association (statistical) and causal logic. Additionally, to evaluate the performance of semantic communication networks, novel key performance indicators metrics that include new "reasoning capacity" measures that could go beyond Shannon's bound to capture the imminent convergence of computing and communication resources. Then, a novel contrastive learning framework is proposed so as to disentangle learnable and memoizable patterns in source data and make the data "semantic-ready". Through the development of a rigorous end-to-end semantic communication network founded on novel concepts from communication theory and AI, along with the proposal of novel performance metrics, this dissertation lays a solid foundation for the advancement of reasoning-driven intelligence in the field of wireless communication and paves the way for a wide range of future applications. Ultimately, the various analytical foundations presented in this dissertation will provide key guidelines that guarantee seamless experiences in future 6G applications, enable a successful deployment of THz wireless systems as a versatile band for integrated communication and sensing, and build future AI-native semantic communication networks. / Doctor of Philosophy / To date, the evolution of wireless networks has been driven by a chase for data rates, i.e., higher download or upload speeds. Nonetheless, future 6G applications (the generation succeeding today's fifth generation 5G), such as the metaverse, extended reality (encompassing augmented, mixed, and virtual reality), and fully autonomous robots and vehicles, necessitate a major leap in the design and functionality of a wireless network. Firstly, wireless networks must be able to perform functionalities that go beyond communications, encompassing control, sensing, and localization. Such functionalities enable a wide range of tasks such as remotely controlling a device, or tracking a mobile equipment with high precision. Secondly, wireless networks must be able to deliver experiences (e.g. provide the user a sense of immersion in a virtual world), in contrast to a mere service. To do so, extreme requirements in terms of data rate, latency, reliability, and sensing resolution must be met. Thirdly, intelligence must be native to wireless networks, which means that they must possess cognitive and reasoning abilities that enable them to think, act, and communicate like human beings. In this dissertation, the three aforementioned key enablers of future 6G experiences are examined. Essentially, one of the focuses of this dissertation is the design, analysis, and optimization of wireless networks operating at the so-called terahertz (THz) frequency band. The THz band is a quasi-optical (close to the visible light spectrum) frequency band that can enable wireless networks to potentially provide the extreme speeds needed (in terms of communications) and the high-resolution sensing. However, such frequency bands tend to be very susceptible to obstacles, humidity, and many other weather conditions. Therefore, this dissertation investigates the potential of such bands in meeting the demands of future 6G applications. Furthermore, novel solutions, enablers, and optimization frameworks are investigated to facilitate the successful deployment of this frequency band. To provide wireless networks with their reasoning ability, this dissertation comprehensively investigates the concept of semantic communications. In contrast to today's traditional communication frameworks that convert our data to binary bits (ones and zeros), semantic communication's goal is to enable networks to communicate meaning (semantics). To successfully engineer and deploy such networks, this dissertation proposes a novel suite of communication theoretic tools and key performance indicators. Subsequently, this dissertation proposes and analyzes a set of novel artificial intelligence (AI) tools that enable wireless networks to be equipped with the aforementioned cognitive and reasoning abilities. The outcomes of this dissertation have the potential to transform the way we interact with technology by catalyzing the deployment of holographic societies, revolutionizing the healthcare via remote augmented surgery, and facilitating the deployment of autonomous vehicles for a safer and more efficient transportation system. Additionally, the advancements in wireless networks and artificial intelligence proposed in this dissertation could also have a significant impact on various other industries, such as manufacturing, education, and defense, by enabling more efficient and intelligent systems. Ultimately, the societal impact of this research is far-reaching and could contribute to creating a more connected and advanced world.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/114899 |
| Date | 02 May 2023 |
| Creators | Chaccour, Christina |
| Contributors | Electrical Engineering, Saad, Walid, Reed, Jeffrey H., Yang, Yaling, Kekatos, Vasileios, Ji, Bo |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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