Research on 6G telecommunication networks has been initiated. Among all potential technology components, the distributed multi-input multioutput (D-MIMO) technology is one of the promising enablers. Due to the new technology solutions, additional methodologies for assessment of electromagnetic field (EMF) exposure need to be developed. This study provides methodologies and results for EMF exposure from D-MIMO operating at 3.5 GHz in an indoor industrial environment using CST Studio Suite®. The D-MIMO access points (APs) are mounted on the 7 m ceiling. The EMF exposure is statistically evaluated in a subvolume that extends up to 2.5 m above the floor, using receiving antennas that are pseudorandomly distributed over space. The resulting EMF exposure levels of DMIMO were compared to those of a ceiling-mounted reference massive MIMO array, considering different receiving antenna orientations and AP densities. The results from zero forcing (ZF) precoding were compared to those based on maximum ratio transmission (MRT) precoding. For a total radiated power of 1 W, the 99th-percentile power density values in the D-MIMO deployment are found to be 2.9 mW/m2 or lower, in all studied cases using the MRT-based precoding. This is about 0.03 % of the EMF exposure limits for the general public specified in international guidelines. The corresponding results from the reference massive MIMO array are found to be 7.7 mW/m2 or lower. In the ZF precoding case, the total radiated power and the EMF exposure levels are reduced and the reduction in the D-MIMO deployment is found greater than the reduction in the massive MIMO array. At the 99th-percentile, the power density value is found to be 0.090 mW/m2 in one of the cases of D-MIMO deployment and 5.1 mW/m2 in the corresponding case with the massive MIMO array. The effects of receiving antenna orientations and AP density on the EMF exposure levels are found to be small. This work benefits further studies by providing estimates of realistic EMF exposure and by demonstrating a simulation method for EMF exposure assessment for D-MIMO. / Forskning inom 6G har påbörjats. Bland de möjliga teknologierna för 6G är distribuerad multi-input multi-output (D-MIMO) ett lovande koncept som möjliggör 6G. På grund av den nya teknologin förväntas nya metoder behövas för bedömning av exponering för elektromagnetiska fält. I denna studie utvecklades och användes metoder för simulering av elektromagnetisk exponering från D-MIMO på frekvensen 3.5 GHz i en industriell inomhusmiljö i CST Studio Suite®. En statistisk behandling av exponeringsnivåer utfördes för pseudoslumpmässiga fördelningar av mottagarantenner. Antennerna i nätverket var placerade intill taket på 7 meters höjd och exponeringsnivån mättes i en delvolym som sträcker sig från golvet till 2.5 m höjd. Jämförelser gjordes med massiv MIMO som referens, och mellan olika vinklar hos mottagarantenner och tätheter av uppkopplingspunkter i nätverket. Antennloberna riktades med zero forcing (ZF) och en metod baserad på maximum ratio transmission (MRT). Med MRT och en total utsänd effekt på 1 W, var 99th-percentilen för elektromagnetisk fältintensitet från D-MIMO 2.9 mW/m2 eller lägre i alla studerade fall, vilket är 0.03 % av den internationella referensnivån för elektromagnetisk exponering. Motsvarande nivå för massiv MIMO var 7.7 mW/m2 eller lägre. Med ZF reducerades den totala utsända effekten och reduktionen i exponeringsnivån var större för D-MIMO än för massiv MIMO. I ett av de studerade fallen var 99th-percentilen från D-MIMO 0.090 mW/m2 , och motsvarande nivå för massivt MIMO var 5.1 mW/m2 eller lägre, Effekterna av vinklar hos mottagarantenner och tätheter av uppkopplingspunkter var liten. Detta arbete bidrar till framtida studier inom ämnet genom att ange uppskattningar av realistiska exponeringsnivåer och genom att demonstrera en metod för simulering av exponeringsnivåer för D-MIMO.

To meet the latest requirements of the 6G standards, several techniques have been proposed in the open literature, such as millimeter waves, terahertz communication, and massive MIMO. In addition to these recent technologies, the use of unmanned aerial vehicles (UAVs) is strongly advocated for 6G networks, as the 6G standard will not be dedicated to broadband services, but will rather be oriented towards reduced geographical cellular coverage. In this context, the deployment of UAVs is considered a key solution for seamless connectivity and reliable coverage. Although UAVs are characterized by their high mobility and their ability to establish line-of-sight links, their use is still impeded by several factors such as weather conditions, their limited computing power, and, most importantly, their limited energy. In this work, we are aiming for the novel technology that enables indefinite wireless power transfer for UAVs using laser beams. We propose a novel UAV deployment strategy, based on which we analyze the overall performance of the system in terms of wireless coverage and provide some useful insights. To this end, we use tractable tools from stochastic geometry to model the complex communication system.

No description available.

The thesis work shows the implementation of sensing and communication, so a basic knowledge of analog and digital communication systems is needed to understand this paper Background. With the increase in smart devices, the bandwidth requirements are increased, which created congestion in the radio spectrum resources. To overcome this spectrum congestion, Integrated Sensing and Communication (ISAC) can be used. This can be achieved by using Orthogonal Frequency-Division Multiplexing (OFDM) signals. In ISAC, both sensing and communication use the same resources,which in turn predominantly improves the efficiency of the spectrum resources usage and reduces the cost of hardware. Objectives. The main aim of this research is to integrate radar sensing and communication using Software Defined Radios (SDR) and GNU Radio. The goal is to design a signal waveform and a receiver algorithm supporting both sensing and communications and then carry out experiments on an SDR unit to evaluate the functionality and performance. Methods. Experimentation is used in this research and is conducted at Ericsson Research Laboratory. The experiment is divided into three major parts. First, to test the sensing functionalities using Frequency Modulated Continuous Waves (FMCW). Second, to test the communication functionalities using OFDM signals. Third, to design the receiver algorithm and signal waveform for ISAC. To verify the dual functional paradigm, the results from the ISAC are compared with the individual test results of sensing and communications using FMCW and OFDM signals. Results. A receiver algorithm is designed to calculate the sensing range and BER of ISAC using an SDR. The results show the possibility of implementing ISAC using OFDM in GNU Radio with SDR. The thesis project can also be viewed as a proof of concept for ISAC on SDR, helping in providing useful information related to radar sensing and communication using OFDM and performance evaluation. Conclusions. The experimental results show the dual-functional waveform for ISAC, helping in the evolution of 5G and beyond 5G communication systems. The identified drawbacks can be used by future researchers working on advanced 5G mobile communication systems to develop more efficient systems. Keywords: Communications, FMCW, GNU Radio, ISAC, OFDM, SDR, Sensing.

Fifth-Generation (5G) and sixth-Generation (6G) are new global wireless standards providing everyone and everything, machines, objects, and devices, with massive network capacity. The technological advances in wireless communication enable 5G and 6G networks to support resource and computation-hungry services such as smart agriculture and smart city applications. Among these advances are two state-of-the-art technologies: Carrier Aggregation (CA) and Multi Access Edge Computing (MEC). CA unlocks new sources of spectrum in both the mid-band and high-band radio frequencies. It provides the unique capability of aggregating several frequency bands for higher peak rates, and increases cell coverage. The latter is obtained by activating the Component Carriers (CC) in low-band and mid-band frequency (below 7 GHz) while 5G high-band (above 24GHz) delivers unprecedented peak rates with poorer Uplink (UL) coverage. MEC provides computing and storage resources with sufficient connectivity close to end users. These execution resources are typically within/at the boundary of access networks providing support for application use cases such as Augmented Reality (AR)/Virtual Reality (VR). The key technology in MEC is task offloading, which enables a user to offload a resource-hungry application to the MEC hosts to reduce the cost (in terms of energy and latency) of processing the application. This thesis focuses on using CA and task offloading in 5G and 6G wireless networks. These advanced infrastructures are an enabler for many broader use cases, e.g., autonomous driving and Internet of Things (IoT) applications. However, the pertinent problems are the high dimensional ones with combinatorial characteristics. Furthermore, the time-varying features of the 5G/6G wireless networks, such as the stochastic nature of the wireless channel, should be concurrently met. The above challenges can be tackled by using data-driven techniques and Machine Learning (ML) algorithms to derive intelligent and autonomous resource management techniques in the 5G/6G wireless networks. The resource management problems in these networks are sequential decision-making problems, additionally with conflicting objectives. Therefore, among the ML algorithms, the ones based on the Reinforcement Learning (RL), constitute a promising tool to make a trade-off between the conflicting objectives of the resource management problems in the 5G/6G wireless networks, are used. This research considers the objective of maximizing the achievable rate and minimizing the users’ transmit power levels in the MEC-enabled network. Additionally, we try to simultaneously maximize the network capacity and improve the network coverage by activating/deactivating the CCs. Compared with the derived schemes in the literature, our contributions are two folded: deriving distributed resource management schemes in 5G/6G wireless networks to efficiently manage the limited spectrum resources and meet the diverse requirements of some resource-hungry applications, and developing intelligent and energy-aware algorithms to improve the performance in terms of energy consumption, delay, and achievable rate.

No description available.

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.

Das Verhalten von durch Graphen oder Graphenoxid (GO) begrenzten Molekülen hat sich, bedingt durch die bemerkenswerten strukturellen und optischen Eigenschaften dieser quasi-zweidimensionalen Materialien, als vielversprechendes Forschungsfeld erwiesen. Die vorliegende Arbeit konzentriert sich auf das Hydrationsverhalten von GO und das Verhalten kleiner, von Graphen begrenzter Moleküle. In dieser Arbeit wurde auf Rasterkraftmikroskopie (SFM) zurückgegriffen, um die GO-Hydration zu untersuchen. Die Ergebnisse zeigen ein graduelles bzw. stufenweises Ansteigen des durchschnittlichen Schichtabstands für relative Luftfeuchtigkeiten (RH) unter halb von 80%, beziehungsweise in flüssigem Wasser. Diese experimentellen Beobachtungen stimmen mit den XRD an vielschichtigem GO in der Literatur überein. Die hier gezeigten Ergebnisse lassen jedoch den angenommenen Einlagerungseffekt, bei der Hydrierung von GO bei geringer RH, außen vor. Stattdessen wird die allmähliche Ausdehnung der kontinuierlichen Einlagerung von Wassermolekülen in den einzelnen GO-Schichten zugeschrieben, während die stufenweise Ausdehnung im komplett in Wasser getauchten Zustand auf das Eindringen einer ganzen Wassermonolage zurückgeführt wird. Andererseits könnte die Grenzfläche zwischen Graphen und dem Substrat ein begrenztes elektrisches Feld aufweisen, das ein weit verbreitetes, auf Ladungstransfer an Grenzflächen zurückzuführendes Phänomen darstellt. Die vorliegende Arbeit behandelt dieses Thema unter Nutzung von Rhodamin 6G (R6G) als Molekül zwischen Graphen und Glimmer, die es begrenzen. Eine Rot-Verschiebung der R6G-Maxima bei geringer RH wird sowohl auf elektrische Felder, die sich auf die Moleküle auswirken, als auch auf mechanische Deformationen der R6G-Struktur an der Grenzschicht zurückgeführt. Die Stärke des elektrischen Feldes wird anhand des Graphen-Raman-Spektrums auf etwa 1 V/nm abgeschätzt. / The behavior of molecules confined by graphene or graphene oxide (GO) has proven to be a promising area of research owing to the remarkable structural and optical properties of these quasi two-dimensional materials. This thesis focuses on the hydration behavior of GO and the behavior of small molecules confined by graphene. In this work, scanning force microscopy (SFM) has been employed to investigate the hydration of GO. The results show a gradual and a step-like increase of the average interlayer distance for relative humidities (RH) below 80% and in liquid water, respectively. These experimental observations are consistent with XRD results on multilayered graphite oxide as reported in the literature. However, the results presented here exclude the postulated interstratification effect, for hydration of GO at low RH. Instead, the gradual expansion is attributed to the continuous incorporation of water molecules into single GO layers, while the step-like expansion when completely immersed in water, is attributed to the insertion of a full monolayer of water. On the other hand, the interface between graphene and its substrate may exhibit a confined electric field, a common phenomenon due to charge transfer at interfaces. In this work, this subject is addressed using Rhodamine 6G (R6G) as a probe molecule confined between graphene and mica. A red shift of the RG6 peaks at low RH is argued to be due to both, electric fields acting on the molecules and mechanical deformation of the R6G structure at the interface. The strength of the field is estimated from the graphene Raman spectra to be on the order of 1 V/nm.

In 6G communication, many state-of-the-art machine learning algorithms are going to be implemented to enhance the performances, including the latency property. In this thesis, we apply Buffer Status Report(BSR) prediction to the uplink scheduling process. The BSR does not include information for data arriving after the transmission of this BSR. Therefore, the base station does not allocate resources for the new arrival data, which increases the latency. To solve this problem, we decide to make BSR predictions at the base station side and allocate more resources than BSRs indicate. It is hard to make an accurate prediction since there are so many features influence the BSRs. Another challenge in this task is that the time intervals are tremendously short (in the order of milliseconds). In other traffic predictions, the traffic data in a long term, such as in a week and month, can be used to predict the periodicity and trend. In addition, many external features, such as the weather, can boost the prediction results. However, when the time is short, it is hard to leverage these features. The datasets provided by Ericsson are collected from real networks. After cleaning the data, we convert the time series forecasting problem into a supervised learning problem. State-of-the-art algorithms such as Random Forest(RF), XGboost, and Long Short Term Memory(LSTM) are leveraged to predict the data arrival rate, and one K-Fold Cross-Validation is followed to validate the models. The results show that even the time intervals are small, the data arrival rate can be predicted and the downlink data, downlink quality indicator and rank indicator can boost the forecasting performance. / I 6G-kommunikation kommer många toppmoderna maskinin lärnings algoritmer att implementeras för att förbättra prestanda, inklusive latensegenskapen. I den här avhandlingen vill vi tillämpa Buffer Status Report (BSR) förutsägelse för schemaläggningsprocessen för upplänkning. BSR innehåller inte information för data som anländer efter överföring av denna BSR. Därför tilldelar basstationen inte resurser för den nya ankomstdatan, vilket ökar latensen. För att lösa detta problem bestämmer vi oss för att göra BSR-förutsägelser på basstationssidan och tilldela fler resurser än vad BSR anger. Det är svårt att göra en exakt förutsägelse eftersom det finns så många funktioner som påverkar BSR. En annan utmaning i denna uppgift är att tidsintervallen är oerhört korta (millisekunder). I andra trafikprognoser kan trafikdata på lång sikt, som under en vecka och månad, användas för att förutsäga periodicitet och trend, och många externa funktioner, såsom väder, kan öka förutsägelseresultaten. Men när tiden är kort är det svårt att utnyttja dessa funktioner. Dataset som tillhandahålls av Ericsson samlas in från riktiga nätverk. Efter rengöring av data konverterar vi tidsserieprognosproblemet till ett övervakat inlärningsproblem. Toppmoderna algoritmer som Random Forest (RF), XGboost och LSTM(Long Short TermMemory) utnyttjas för att förutsäga data ankomst astigheten och en K-Fold Cross-Validation följs för att validera modellerna. Resultaten visar att även tidsintervallen är små, datainkomsthastigheten kan förutsägas och nedlänksdata, kvalitetsindikator för nedlänk och rangindikator kan öka prognosprestandan.

Durch alternierende oder simultane Plasmapolymerisation und Farbstoffsublimation wurden Plasmapolymerschichten mit eingelagerten Farbstoffmolekülen hergestellt. Als Farbstoff wurde Rhodamin 6G verwendet. Die Absorptions- und Fluoreszenzcharakteristika der Schichten wurden untersucht und mit denen einfacher Farbstoffschichten verglichen. Dabei wurde die Menge der eingelagerten Farbstoffmoleküle variiert. Es zeigt sich, daß sich die Absorptionsbande des eingelagerten Farbstoffs bei den unterschiedlichen Schichten kaum verändert. Die Fluoreszenzspektren der Schichten mit wenigen eingelagerten Farbstoffmolekülen zeigen dasgleiche Verhalten wie Rhodamin 6G in Lösung. Mit steigender Farbstoffmenge wird die Fluoreszenzbande rotverschoben und es entsteht eine weitere Fluoreszenzbande. Diese wird mit Hilfe eines sequentiellen Energietransfers zu modifizierten Molekülen beschrieben. Mit Hilfe der zeitaufgelösten Fluoreszenzspektroskopie konnte der zeitliche Verlauf der Fluoreszenzintensität gemessen werden. Dabei ist der Fluoreszenzzerfall deutlich nicht-exponentiell. Die Zerfallskurven wurden mit einer gestreckten Exponentialfunktion angepaßt.