This thesis aims to find general principles governing the behaviour of biological systems, with a particular emphasis in the communicational (social) aspect of these systems. Communication between biological entities plays a major role in their evolution, enabling them to exchange information about their environment and thereby improving their chances of survival. Communication also plays a pivotal role in the organisation of populations of organisms, clearly observed in social insects, but present also at least in bacteria, plants, fungi, animals and humans. It is also theorised that the genetic code is a by-product of the establishment of an innovation-sharing protocol between primitive cells [Vetsigian et al., 2006]. This thesis is mainly concerned with identifying necessary conditions for the emergence of communicational codes, and the problems that arise with their establishment. For this purpose, we introduce an information-theoretic framework where species maximise their growth rate by following a Kelly-gambling strategy to bet on environmental conditions. Information theory provides a powerful tool for abstracting away mechanisms and for focusing on hard limits of a system's dynamics which cannot be circumvented. We begin by exploring the relation between information exchange and limited resources. We show that a transition from cooperation to antagonism in the exchange of environmental information follows from a change in the availability of resources, from abundant to scarce. We then assume a non-competitive scenario with abundance of resources, where conflicts in a population occur only at a communicational (informational) level, rather than on the physical level, such as competing for (physical) resources. However, traditional Shannon communication is non-semantic, as opposed to the semantic communication observed in biological systems, which is necessary for capturing conflicts in communication. In the traditional use of information theory, it is assumed that every organism knows how to \interpret" the information offered by other organisms. However, this assumes that one \knows" which other organisms one observes, and thus which code they use. In our model, however, we wish to preclude that: namely, we will do away with the assumption that the identity of the organisms who send the messages and those who receive them is known, and the resulting usable information is therefore influenced by the universality of the code used and by which organisms an organism is \listening" to. We introduce a model which captures semantic communication in information-theoretic terms, where organisms talk to each other in a communication network. We show that, for particular population structures, when organisms cannot identify which other organisms they talk to, the adoption of a universal code emerges as a solution for full interpretation of the shared information. However, the evolution and establishment of universal codes for communication introduces vulnerabilities: organisms can be exploited by parasites. We de ne two types of parasites whose strategies have different levels of complexity and study the co-evolution of a host (the population) and a parasite by optimising their respective objective functions in stages. First, we consider a disruptive parasite (a troll) that inflicts harm in a host by minimising a population's mutual understanding, and then a more complex parasite, which manipulates the members of the population via their codes (the puppetmaster). We show emergent characterisations of both parasites, as well as which host configurations are robust against parasites and show adaptive properties. This thesis introduces a framework which allows the study of informational properties in the host-parasite co-evolution, where the rules of the parasite's habitat, the host, are the outcome of an evolutionary process, and where these very same rules are those that allow the parasite to exploit the host.

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.

With the era of wirelessly connected Internet of Things (IoT) devices on the horizon, eective data processing algorithms for IoT devices are of increasing importance. IoT devices often have limited power and computational resources, making data processing on the device unfeasible. Computational ooading, where the raw data is transmitted to a separate server, places a high load on the communication network, which in some cases may be prohibitively expensive. A split computing framework where some data pre-processing is done on the device, but the bulk of computations are done on a server at the network edge, provides a compromise between these limitations. Here, we employ a split computing framework in a semantic communication setting, where the semantic task is image classification. The system should fulfill three design requirements: low computational load on the IoT device, low load on the communication network, and good classification performance. We investigate the performance of two neural network structures: the first network is based on the VGG16 image classification network, and the second is the VGG16 network is augmented by separate encoder and decoder networks. The results are promising under both ideal and non-ideal channel conditions, where the first network gives good classification performance and low load on the communication network. The second network has low load on the IoT device, but surprisingly poor classification performance. Finally, we provide important insights into design choices and pitfalls, particularly reagrding network architecture and training, and hope that these results can aid future work in semantic communication systems. / I takt med att allt fler av våra system kopplas upp för kommunikation via internet, så kallad Internet of Things (IoT), får eektiva databehandlingsalgoritmer för dessa enheter av allt större betydelse. IoT-enheter har ofta begränsat minne, batteritid, och beräkningsresurser, vilket försvårar databehandling på enheten. Beräkningsavlastning, där rådata skickas till en separat server för behandling, kan leda till en hög belastning på kommunikationsnätverket, vilket i vissa fall är kostsamt. Att dela upp beräkningarna, där viss bearbetning av data görs på enheten men huvuddelen av beräkningarna görs på en server, är kompromiss mellan dessa två begränsningar. Här använder vi ett delat beräkningsramverk för semantisk kommunikation, där den semantiska uppgiften är bildklassificering. Systemet ska uppfylla tre designkrav: låg arbetsbelastning på IoT-enheten, låg belastning på kommunikationsnätverket och god klassificeringsprestanda. Vi undersöker två neurala nätverksstrukturer: den första är baserad på bildklassificeraren VGG16, och i den andra är VGG16-nätverket utökat med separata kodar- och avkodarnätverk. Resultaten är lovande under både ideala och icke-ideala förhållanden i kommunikationskanalen, där det första nätverket ger god klassificeringsprestanda och låg belastning på kommunikationsnätverket. Det andra nätverket har låg belastning på IoT-enheten, men överraskande dålig klassificeringsprestanda. Vi ger även viktiga insikter i designval och fallgropar, specifikt gällande nätverkens arkitektur och träning, och hoppas att dessa resultat kan gagna framtida arbete inom semantiska kommunikationssystem.