The growing traffic and global bandwidth shortage for broadband cellular communi-cation networks has motivated to explore the underutilized millimeter wave frequencyspectrum for future communications. Fifth generation (5G) is the key to empow-ering new services and use cases for people, businesses, and society at large. Withunprecedented speed and flexibility, 5G carries more data with greater reliability andresponsiveness than ever before. As 5G new radio (NR) begins to take full advantageof the high-band spectrum, i.e, the millimeter wave frequencies, new challenges arecreated. While millimeter waves offer broader bandwidth and high spatial resolution,the drawback is that the millimeter waves experience higher attenuation due to pathloss and are more prone to absorption, interference and weather conditions, thereforelimiting cell coverage.This thesis is an attempt to increase the 5G cell coverage by implementing ananalogue beamforming router in a cell. Beamforming router acts like a relay, whichextends the range of the 5G cell whenever needed, according to the position of theUser Equipment (UE) based on the information received from the gNodeB (gNB,logical 5G radio node). This thesis is investigating the downlink Signal-to-Noise Ratio(SNR) gain and thus possible increase in the data rate. Simulation and validation ofthe overall performance is done using MATLAB. The outcome of this study may beused to increase the 5G cell coverage if it is implemented in a real.

Dissemination of disinformation is one of the biggest threats to modern society. Specifically, studies have shown that conspiracy theories endanger civil discourse and undermine trust towards information and social institutions. Researchers have previously focused on analyzing conspiratorial groups from an outsider’s perspective and in relation to echo chambers. Therefore, the purpose of this essay is to offer an alternative perspective that focuses on how an anti 5G group on Facebook creates a group identity — a sense of belonging — and how a strong community influences the members’ opinions regarding 5G. A netnographic approach was used to observe the group and analyze the first two question statements, whilst digital interaction analysis was used to analyze the last question statement. Thereafter, Conspiracist Ideation and Sense of Community were applied as our theoretical framework to examine and understand the material. The results show that the members foster a group identity and a sense of belonging by promoting norms and creating a structure for how one should behave and think in the group. These aspects created an online space where members could receive emotional support and feel safe to express controversial opinions. We could not determine whether the sense of community or group identity affected members’ opinions. We can, however, verify that a strong sense of belonging within the group encouraged discussions about conspiratorial thinking. With this in mind, we suggest that a strong sense of community might have a bigger impact on conspiracy groups and echo chambers than previously thought. Further research should focus on a strong sense of community creating echo chambers, rather than echo chambers creating communities. Key words: anti-5G, conspiracy theories, netnography, community online

With an ever-increasing percentage of the human population connected to the internet, the amount of data produced and processed is at an all-time high. Edge Computing has emerged as a paradigm to handle this growth and, combined with 5G, enables complex time-sensitive applications running on resource-restricted devices. This master thesis investigates the use of WebAssembly in the context of computa¬tional offloading at the Edge. The focus is on utilizing WebAssembly to move computa¬tional heavy parts of a system from an end device to an Edge Server. An objective is to improve program performance by reducing the execution time and energy consumption on the end device. A proof-of-concept offloading system is developed to research this. The system is evaluated on three different use cases; calculating Fibonacci numbers, matrix multipli¬cation, and image recognition. Each use case is tested on a Raspberry Pi 3 and Pi 4 comparing execution of the WebAssembly module both locally and offloaded. Each test will also run natively on both the server and the end device to provide some baseline for comparison.

Communications over millimeter wave (mmWave) frequencies is a key component of the fifth generation (5G) cellular networks due to the large bandwidth available at mmWave bands. Thanks to the short wavelength of mmWave bands, large antenna arrays (32 to 256 elements are common) can be mounted at the transceivers. The array sizes are typical of a massive MIMO communication system, which makes fully digital beamforming difficult to implement due to high power consumption and hardware cost. This motivates the development of hybrid beamforming due to its versatile tradeoff between implementation cost (including hardware cost and power consumption) and system performance. However, due to the non-convex constraints on hardware (phase shifters), finding the global optima for hybrid beamforming design is often intractable. In this thesis, we focus on hybrid beamforming design for mmWave cellular communications both narrowband and wideband scenarios are considered. Starting from narrowband SU-MIMO mmWave communications, we propose a Gram-Schmidt orthogonalization (GSO) aided hybrid precoding algorithm to reduce computation complexity. GSO is a recursive process that depends on the order in which the matrix columns are selected. A heuristic solution to the order of column selection is suggested according to the array response vector along which the full digital precoder has the maximum projection. The proposed algorithm, not only constrained to uniform linear arrays (ULAs), can avoid the matrix inversion in designing the digital precoder compared to the orthogonal matching pursuit (OMP) algorithm. For the narrowband MU-MIMO mmWave communications, we propose an interference cancellation (IC) framework on hybrid beamforming design for downlink mmWave multi-user massive MIMO system. Based on the proposed framework, three successive interference cancellation (SIC) aided hybrid beamforming algorithms are proposed to deal with inter-user and intra-user interference. Furthermore, the optimal detection order of data streams is derived according to the post-detection signal-to-interference- plus-noise ratio (SINR). When considering wideband MU-MIMO mmWave communications, how to design a common RF beamformer across all subcarriers becomes the main challenge. Furthermore, the common RF beamformer in wideband channels leads to the need of more effective baseband schemes. By adopting a relaxation of the original mutual information and spectral efficiency maximization problems at the transceiver, we design the radio frequency (RF) precoder and combiner by leveraging the average of the covariance matrices of frequency domain channels, then a SIC aided baseband precoder and combiner are proposed to eliminate inter-user and intra-user interference / Graduate

As the complexity of modern cellular networks continuously increases along with the evolution of technologies and the quick explosion of mobile data traffic, conventional large scale system level simulations and analytical tools become either too complicated or less tractable and accurate. Therefore, novel analytical models are actively pursued. In recent years, stochastic geometry models have been recognized as powerful tools to analyze the key performance metrics of cellular networks. In this dissertation, stochastic geometry based analytical models are developed to analyze the performance of some key technologies proposed for 5G mobile networks. Particularly, Device-to-Device (D2D) communication, Non-orthogonal multiple access (NOMA), and ultra-dense networks (UDNs) are investigated and analyzed by stochastic geometry models, more specifically, Poisson Point Process (PPP) models. D2D communication enables direct communication between mobile users in proximity to each other bypassing base station (BS). Embedding D2D communication into existing cellular networks brings many benefits such as improving spectrum efficiency, decreasing power energy consumption, and enabling novel location-based services. However, these benefits may not be fully exploited if the co-channel interference among D2D users and cellular users is not properly tackled. In this dissertation, various frequency reuse and power control schemes are proposed, aiming at mitigating the interference between D2D users and conventional cellular users. The performance gain of proposed schemes is analyzed on a system modeled by a 2-tier PPP and validated by numerical simulations. NOMA is a promising radio access technology for 5G cellular networks. Different with widely applied orthogonal multiple access (OMA) such as orthogonal frequency division multiple access (OFDMA) and single carrier frequency division multiple access (SC-FDMA), NOMA allows multiple users to use the same frequency/time resource and offers many advantages such as improving spectral efficiency, enhancing connectivity, providing higher cell-edge throughput, and reducing transmission latency. Although some initial performance analysis has been done on NOMA with single cell scenario, the system level performance of NOMA in a multi-cell scenario is not investigated in existing work. In this dissertation, analytical frameworks are developed to evaluate the performance of a wireless network with NOMA on both downlink and uplink. Distinguished from existing publications on NOMA, the framework developed in this dissertation is the first one that takes inter-cell interference into consideration. UDN is another key technology for 5G wireless networks to achieve high capacity and coverage. Due to the existence of line-of-sight (LoS)/non-line-of-sight (NLoS) propagation and bounded path loss behavior in UDN networks, the tractability of the original PPP model diminishes when analyzing the performance of UDNs. Therefore, a dominant BS (base station)-based approximation model is developed in this dissertation. By applying reasonable mathematical approximations, the tractability of the PPP model is preserved and the closed form solution can be derived. The numerical results demonstrate that the developed analytical model is accurate in a wide range of network densities. The analysis conducted in this dissertation demonstrates that stochastic geometry models can serve as powerful tools to analyze the performance of 5G technologies in a dense wireless network deployment. The frameworks developed in this dissertation provide general yet powerful analytical tools that can be readily extended to facilitate other research in wireless networks.

As 5G and O-RAN become more widely used, the number of user equipment requesting access to the network will increase. This will require operators to expand their 5G solutions by purchasing more hardware to handle the increase in demand. The acquisition of new hardware will have both an economic and an environmental impact. Hardware is costly for operators, both in initial cost and when operating it. There is also a significant energy cost associated, which has a negative environmental impact.     This thesis explores the benefits of more advanced control over the path taken within the Radio Access Network, with the goal of increasing the number of user equipment able to connect to a static set of hardware. The control comes from new algorithms designed with the intuition that providing connections with only the bare essentials and nothing more would, in theory, increase the capacity of the whole network. Three algorithms were tested, with one representing a basic control method of selecting the first valid connection, and the other two were built on the intuition of the worst acceptable connection.     The three algorithms were tested on four different shapes of network configuration at four different sizes. The tests were run on a graph data structure implemented in C++ that represents the logical paths a connection could take. This resulted in a noticeable improvement in networks that exhibited a triangular structure, with more units as one moved toward the edge of the network. The largest improvement observed managed to fit 18.9% more units into the network.

This work addresses the challenge of heterogeneity in blockchain mining, particularly in the context of consortium and private blockchains. The motivation stems from ensuring fairness and efficiency in blockchain technology's Proof of Work (PoW) consensus mechanism. Existing consensus algorithms, such as PoW, PoS, and PoB, have succeeded in public blockchains but face challenges due to heterogeneous miners. This thesis highlights the significance of considering miners' computing power and resources in PoW consensus mechanisms to enhance efficiency and fairness. It explores the implications of heterogeneity in blockchain mining in various applications, such as Federated Learning (FL), which aims to train machine learning models across distributed devices collaboratively. The research objectives of this work involve developing novel RL-based techniques to address the heterogeneity problem in consortium blockchains. Two proposed RL-based approaches, RL based Miner Selection (RL-MS) and RL based Miner and Difficulty Selection (RL-MDS), focus on selecting miners and dynamically adapting the difficulty of PoW based on the computing power of the chosen miners. The contributions of this research work include the proposed RL-based techniques, modifications to the Ethereum code for dynamic adaptation of Proof of Work Difficulty (PoW-D), integration of the Commonwealth Cyber Initiative (CCI) xG testbed with an AI/ML framework, implementation of a simulator for experimentation, and evaluation of different RL algorithms. The research also includes additional contributions in Open Radio Access Network (O-RAN) and smart cities. The proposed research has significant implications for achieving fairness and efficiency in blockchain mining in consortium and private blockchains. By leveraging reinforcement learning techniques and considering the heterogeneity of miners, this work contributes to improving the consensus mechanisms and performance of blockchain-based systems. / Master of Science / Technological Advancement has led to devices having powerful yet heterogeneous computational resources. Due to the heterogeneity in the compute of miner nodes in a blockchain, there is unfairness in the PoW Consensus mechanism. More powerful devices have a higher chance of mining and gaining from the mining process. Additionally, the PoW consensus introduces a delay due to the time to mine and block propagation time. This work uses Reinforcement Learning to solve the challenge of heterogeneity in a private Ethereum blockchain. It also introduces a time constraint to ensure efficient blockchain performance for time-critical applications.

This master thesis aims for Inter-carrier interference (ICI) mitigation in orthogonal frequency division multiplexing (OFDM) system by considering designs of frequency domain cyclic extension(FDCE) and optimal windowing pulse shape. Although OFDM system has been put forward in the 1970s, it has just emerged in 4G. In the early stage, it has been restricted by its high computational complexity. With the discovery that modulation and demodulation process of OFDM can be realized by discrete Fourier transform (DFT) and inverse discrete Fourier transform (IDFT), it is widely used in 4G and 5G-New Ratio (NR). Based on OFDM system, a variety of derivative systems are further proposed and applied. With the development of 5G technology in the mobile communication, the requirement of signal propagation between high-speed mobile user and base station (BS) is higher and higher. With the increase of the moving speed of objects, the frequency shift caused by Doppler eff ect can not be underestimated. ICI caused by Doppler shift is becoming more and more serious. Therefore, how to eliminate the ICI caused by Doppler shift has become an inevitable potential problem. In this thesis, two eff ective approaches for ICI mitigation have been explored and studied. By adding FDCE and optimal windowing pulse shape, the system performance is analyzed and the system simulation is constructed in MATLAB.

The vast majority of computing hardware platforms available today are not desktop PCs. They are embedded systems, sensors and small specialized pieces of hardware present in almost every digital product available today. Due to the massive amount of information available through these devices we can find new and exciting ways to apply and benefit from machine learning. Many of these computing devices have specialized, resource-constrained architectures and it might be problematic to perform complicated computations. If such a system is under heavy load or has restricted performance, computational power is a valuable resource and costly algorithms must be avoided. \\This master thesis will present an in-depth study investigating the trade-offs between precision, latency and memory consumption of a selected set of machine learning algorithms implemented on a resource constrained multi-core telecom hardware platform. This report includes motivations for the selected algorithms, discusses the results of the algorithms execution on the hardware platform and offers conclusions relevant to further developments. / Majoriteten av beräkningsplattformarna som finns tillgängliga idag är inte stationära bordsdatorer. De är inbyggda system, sensorer och små specialiserade hårdvaror som finns i nästan alla digitala produkter tillgängliga idag. På grund av den enorma mängden information som finns tillgänglig via dessa enheter kan vi hitta nya och spännande sätt att dra nytta av maskininlärning. Många av dessa datorer har specialiserade, resursbegränsade arkitekturer och det kan vara problematiskt att utföra de komplicerade beräkningar som behövs. Om ett sådant system är tungt belastat eller har begränsad prestanda, är beräkningskraft en värdefull resurs och kostsamma algoritmer måste undvikas. \\ Detta masterprojekt kommer att presentera en djupgående studie som undersöker avvägningarna mellan precision, latens och minneskonsumtion av en utvald uppsättning maskininlärningsalgoritmer implementerade på en resursbegränsad flerkärnig telekom-maskinvaruplattform. Denna rapport innehåller motivationer för de valda algoritmerna, diskuterar resultaten av algoritmerna på hårdvaruplattformen och presenterar slutsatser som är relevanta för vidareutveckling.

The 3rd generation partnership program (3GPP) has in recent years started working on integratingnon-terrestrial networks (NTN) into the 5G eco-system. This thesis focuses on the mobility between NTN and TN, which is of great importance if 5G NTN is to provide seamless and limitless connectivity. The target of this thesis is to understand and improve the mobility performance ofnon-terrestrial and terrestrial networks in a heterogeneous scenario. We first analyze data from system-level simulations of rural deployment scenarios when altering the parameters of the A3 measurement event and and then we further evaluate the use of a new NTN-specific distance-based measurement event, the D1 measurement event. We also evaluate the impact of needing toperform GNSS measurements when performing handovers from a terrestrial to a non-terrestrialnetwork. The results show that acquiring GNSS data during the handover procedure significantly increases handover delay time but does not heavily impact overall network performance. Additionally, the results show that by changing the parameters of the A3 measurement event and using the D1measurement event, ping-pong events between NTN-TN and unnecessary handovers to NTN canbe significantly reduced and improve the overall network performance.