This thesis deals with the topic of network slicing technology in 5G networks, mainly on the RAN part. In the theoretical part, basic principles of 5G network slicing in core network part and RAN part are presented. Practical part contains a simulation scenario created in NS3 simulator with LENA 5G module. Results of this simulation are presented and discussed with the emphasis on RAN slicing.

Fifth Generation (5G) introduces technologies that expedite the adoption of mobile networks, such as densely connected devices, ultra-fast data rate, low latency and more. With those visions in 5G and 6G in the next step, the need for a higher transmission rate and lower latency is more demanding, possibly breaking Moore’s law. With Artificial Intelligence (AI) techniques becoming mature in the past decade, optimizing resource allocation in the network has become a highly demanding problem for Mobile Network Operators (MNOs) to provide better Quality of Service (QoS) with less cost. This thesis proposes a Reinforcement Learning (RL) solution on bandwidth allocation for network slicing integration in disaggregated Open Radio Access Network (O-RAN) architecture. O-RAN redefines traditional Radio Access Network (RAN) elements into smaller components with detailed functional specifications. The concept of open modularization leads to greater potential for managing resources of different network slices. In 5G mobile networks, there are three major types of network slices, Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC). Each network slice has different features in the 5G network; therefore, the resources can be relocated depending on different needs. The virtualization of O-RAN divides the RAN into smaller function groups. This helps the network slices to divide the shared resources further down. Compared to traditional sequential signal processing, allocating dedicated resources for each network slice can improve the performance individually. In addition, shared resources can be customized statically based on the feature requirement of each slice. To further enhance the bandwidth utilization on the disaggregated O-RAN, a RL algorithm is proposed in this thesis on midhaul bandwidth allocation shared between Centralized Unit (CU) and Distributed Unit (DU). A Python-based simulator has been implemented considering several types of mobile User Equipment (UE)s for this thesis. The simulator is later integrated with the proposed Q-learning model. The RL model finds the optimization on bandwidth allocation in midhaul between Edge Open Cloud (O-Cloud)s (DUs) and Regional O-Cloud (CU). The results show up to 50% improvement in the throughput of the targeted slice, fairness to other slices, and overall bandwidth utilization on the O-Clouds. In addition, the UE QoS has a significant improvement in terms of transmission time.

The fifth-generation (5G) of mobile communication networks are expected to support a large number of vertical industries requiring services with diverging requirements. To accommodate this, mobile networks are undergoing a significant transformation to enable a variety of services to coexist on the same infrastructure through network slicing. Additionally, the introduction of distributed user-plane and multi-access edge computing (MEC) technology allows the deployment of virtualised applications close to the network edge. The first part of this dissertation focuses on end-to-end network slice provisioning for various vertical industries with different service requirements. Two slice provisioning strategies are explored, by formulating a mixed integer linear programming (MILP) problem. Further, a genetic algorithm (GA)-based approach is proposed with the aim to improve search-space exploration. Simulation results show that the proposed approach is effective in providing near-optimal solutions while drastically reducing computational complexity. In a later stage, the study focuses on building a measurement-based digital twin (DT) for the highly heterogeneous MEC ecosystem. The DT operates as an intermediate and collaborative layer, enabling the orchestration layer to better understand network behavior before making changes to the physical network. Assisted by proper AI/ML solutions, the DT is envisioned to play a crucial role in automated network management. The study utilizes an emulated and physical test-bed to gather network key performance indicators (KPIs) and demonstrates the potential of graph neural network (GNN) in enabling closed loop automation with the help of DT. These findings offer a foundation for future research in the area of DT models and carbon footprint-aware orchestration.

The concept of network slicing has been introduced in order to enable mobile networks to accommodate multiple heterogeneous use cases that are anticipated to be served within a single physical infrastructure. The slices are end-to-end virtual networks that share the resources of a physical network, spanning the core network (CN) and the radio access network (RAN). RAN slicing can be more challenging than CN slicing as the former deals with the distribution of radio resources, where the capacity is not constant over time and is hard to extend. The main challenge in RAN slicing is to simultaneously improve multiplexing gains while assuring enough isolation between slices, meaning one of the slices cannot negatively influence other slices. In this work, a flexible and configurable framework for RAN slicing is provided, where diverse requirements of slices are taken into account, and slice management algorithms adjust the control parameters of different radio resource management (RRM) mechanisms to satisfy the slices' service level agreements (SLAs). A new entity that translates the key performance indicator (KPI) targets of the SLAs to the control parameters is introduced and is called RAN slice orchestrator. Diverse algorithms governing this entity are introduced, which range from heuristics-based to model-free methods. Besides, a protection mechanism is constructed to prevent the negative influences of slices on each other's performances. The simulation-based analysis demonstrates the feasibility of slicing the RAN with multiplexing gains and slice isolation.

The Internet of today is very different from how it used to be. Modern networked applications are becoming increasingly diverse. Consequently, a variety of requirements must be met by the network. Efforts to make the underlying mechanisms of the Internet more flexible have therefore been made to adapt to this diversification. In this thesis, we explore how information about application requirements can be leveraged to optimize the network protocol stack of end-hosts during run-time. In addition, we improve the visibility of the network to the end-host in order to enable additional flexibility in the usage of the network's resources. We conduct tests in real-world testbeds and examine how services might be developed to optimize latency, throughput, and availability for various network traffic scenarios, including 360-degree video streaming, drone autopilots, and connected vehicles. We show how multi-connectivity, where the end-host is connected via multiple network paths simultaneously, may be used to significantly reduce latency and increase availability, while minimizing the overhead imposed on the network by carefully considering the network selection process. Furthermore, we describe an architecture that allows the user equipment and network functionality inside the 5G core network to cooperatively optimize the resource usage of the network. / The Internet of today is very different from how it used to be. Modern networked applications are becoming increasingly diverse. Consequently, a variety of requirements must be met by the network. This presents a massive challenge, since the Internet was originally designed on best-effort principle.  To address this challenge, we explore how Internet end-hosts can flexibly adapt to the needs of individual applications, by dynamically configuring the network protocol stack during run-time. In addition, we improve the visibility of the network, allowing end-hosts to better utilize the resources of the network.  We conduct tests in real-world testbeds and examine how services might be developed to optimize latency, throughput, and availability for various network traffic scenarios. We also show how multiple network paths can be used simultaneously to significantly reduce latency and increase availability, while minimizing the overhead imposed on the network. Furthermore, we describe an architecture that allows the user equipment and network functionality inside the 5G core network to cooperatively optimize the resource usage of the network. / <p>Paper II was published as a manuscript in the thesis. It is an extended version of the paper, which adds additional material that had to be cut from the original paper due to page limit restrictions.</p>

A significant development in wireless communication and artificial intelligence has been made possible by the combination of 5G networks with deep learning methods. This paper explores the complex interactions between these areas, concentrating on the dangers that adversarial attacks represent in the context of 5G network slicing. Multiclass classification models are created first, utilizing CNN, LSTM, and MLP architectures using a thorough three-phase process. Real adversarial attacks like FGSM, CW, BIM, and PGD are subsequently created to highlight the models' vulnerability to manipulation. The result highlights the need for strong protection measures by highlighting the upsetting potential of these attacks. The recommended defensive methods are addressed in the last stage, providing potential countermeasures to adversary threats. This study emphasizes the significance of taking into account ecological and societal implications while accepting such breakthroughs by bridging the technology and sustainability components. Integrating sustainability into the conversation becomes increasingly important as we advance the boundaries of technological innovation. By doing this, it is provided the foundation for a future that balances technical advancement with ethical progress, promoting a more robust and inclusive digital environment.

5G is expected to provide a high-performance and highly efficient network to prominent industry verticals with ubiquitous access to a wide range of services with orders of magnitude of improvement over 4G. Network slicing, which allocates network resources according to users’ specific requirements, is a key feature to fulfil the diversity of requirements in 5G network. However, network slicing also brings more orchestration and difficulty in monitoring and admission control. Although the problem of admission control has been extensively studied, those research take measurements for granted. Fixed high monitoring frequency can waste system resources, while low monitoring frequency (low level of observability) can lead to insufficient information for good admission control decisions. To achieve efficient admission control in 5G, we consider the impact of configurable observability, i.e. control observed information by configuring measurement frequency, is worth investigating. Generally, we believe more measurements provide more information about the monitored system, thus enabling a capable decision-maker to have better decisions. However, more measurements also bring more monitoring overhead. To study the problem of configurable observability, we can dynamically decide what measurements to monitor and their frequencies to achieve efficient admission control. In the problem of admission control with configurable observability, the objective is to minimize monitoring overhead while maintaining enough information to make proper admission control decisions. In this thesis, we propose using the Deep Reinforcement Learning (DRL) method to achieve efficient admission control in a simulated 5G end-to-end network, including core network, radio access network and four dynamic UEs. The proposed method is evaluated by comparing with baseline methods using different performance metrics, and then the results are discussed. With experiments, the proposed method demonstrates the ability to learn from interaction with the simulated environment and have good performance in admission control and used low measurement frequencies. After 11000 steps of learning, the proposed DRL agents generally achieve better performance than the threshold-based baseline agent, which takes admission decisions based on combined threshold conditions on RTT and throughput. Furthermore, the DRL agents that take non-zero measurement costs into consideration uses much lower measurement frequencies than DRL agents that take measurement costs as zero. / 5G förväntas ge ett högpresterande och högeffektivt nätverk till framstående industrivertikaler genom allmän tillgång till ett brett utbud av tjänster, med förbättringar i storleksordningar jämfört med 4G. Network slicing, som allokerar nätverksresurser enligt specifika användarkrav, är en nyckelfunktion för att uppfylla mångfalden av krav i 5G-nätverk. Network slicing kräver däremot också mer orkestrering och medför svårigheter med övervakning och tillträdeskontroll. Även om problemet med tillträdeskontroll har studerats ingående, tar de studierna mätfrekvenser för givet. Detta trots att hög övervakningsfrekvens kan slösa systemresurser, medan låg övervakningsfrekvens (låg nivå av observerbarhet) kan leda till otillräcklig information för att ta bra beslut om antagningskontroll. För att uppnå effektiv tillträdeskontroll i 5G anser vi att effekten av konfigurerbar observerbarhet, det vill säga att kontrollera observerad information genom att konfigurera mätfrekvens, är värt att undersöka. Generellt tror vi att fler mätningar ger mer information om det övervakade systemet, vilket gör det möjligt för en kompetent beslutsfattare att fatta bättre beslut. Men fler mätningar ger också högre övervakningskostnader. För att studera problemet med konfigurerbar observerbarhet kan vi dynamiskt bestämma vilka mätningar som ska övervakas och deras frekvenser för att uppnå effektiv tillträdeskontroll. I problemet med tillträdeskontroll med konfigurerbar observerbarhet är målet att minimera övervakningskostnader samtidigt som tillräckligt med information bibehålls för att fatta korrekta beslut om tillträdeskontroll. I denna avhandling föreslår vi att använda Deep Reinforcement Learning (DRL)-metoden för att uppnå effektiv tillträdeskontroll i ett simulerat 5G-änd-till-änd-nätverk, inklusive kärnnät, radioaccessnätverk och fyra dynamiska användarenheter. Den föreslagna metoden utvärderas genom att jämföra med standardmetoder som använder olika prestationsmått, varpå resultaten diskuteras. I experiment visar den föreslagna metoden förmågan att lära av interaktion med den simulerade miljön och ha god prestanda i tillträdeskontroll och använda låga mätfrekvenser. Efter 11 000 inlärningssteg uppnår de föreslagna DRL-agenterna i allmänhet bättre prestanda än den tröskelbaserade standardagenten, som fattar tillträdesbeslut baserat på kombinerade tröskelvillkor för RTT och throughput. Dessutom använder de DRL-agenter som tar hänsyn till nollskilda mätkostnader, mycket lägre mätfrekvenser än DRL-agenter som tar mätkostnaderna som noll.

To solve the bottleneck problem of resource utilization and user experience quality in mobile communication networks, 5G introduces network slicing to cope with the huge resource demand of users. To further improve the quality of service for users with different needs, a new fairness definition based on service level objective is introduced. On this basis, a network slicing dynamic resource scheduling strategy based on the greedy algorithm is designed, and the actual application scenarios of slicing scheduling and user scheduling are simplified into a two-layer model, namely the slicing-user model, and combined with the greedy algorithm to make the service weight value. Combine the largest slice and the user with the highest priority, and complete the matching service. The advantage of this method is various system resources can be fairly allocated according to the same proportion to users. Through the optimal combination of each slice and user, the resources of the entire system can be fairly allocated to users with different needs. Python simulation results showed that the newly proposed network slicing dynamic resource scheduling mechanism based on the greedy algorithm can meet the different needs of users and achieve short term fairness, where the users get a fair share of the resource by each missing their SLO by a similar percentage, so as to better meet the needs of users. / För att lösa flaskhalsproblemet med resursanvändning och användarupplevelsekvalitet i mobilkommunikationsnät introducerar 5G nätverksskivning för att klara användarnas enorma resursbehov. För att ytterligare förbättra servicekvaliteten för användare med olika behov införs en ny rättvisedefinition baserad på servicenivåmål. På grundval av detta utformas en dynamisk resursplaneringsstrategi för nätverksskivning baserad på den giriga algoritmen, och de faktiska applikationsscenarierna för skivningsplanering och användarschemaläggning förenklas till en tvåskiktsmodell, nämligen skivningsanvändarmodellen, och kombineras med girig algoritm för att göra tjänstens viktvärde. Kombinera den största delen och användaren med högsta prioritet och slutför motsvarande tjänst. Fördelen med denna metod är att olika systemresurser kan fördelas rättvist enligt samma andel, och genom den bästa kombinationen av varje segment och användare kan hela systemets resurser fördelas rättvist till användare med olika behov. Pythons simuleringsresultat visar att den nyligen föreslagna nätverksskärningsdynamiska resursplaneringsmekanismen baserad på den giriga algoritmen kan tillgodose användarnas olika behov och uppnå kortsiktig rättvisa där användarna får en rättvis andel av resursen genom att var och en saknar sin SLO med en liknande procentsats , för att bättre möta användarnas behov.

In a previous work, a distributed safety framework supported by edge computing was developed to enable real-time response of robots that collaborate with humans in the Human-Robot Collaboration (HRC) scenario. However, as the number of robots in the automated warehouse increases, the network is easier to induce the congestion. A network infrastructure that can fulfill the automated warehouse needs is therefore desired. This work develops network slicing technology in the aforementioned network infrastructure and investigates its application in the automated warehouse scenario. The goal is to improve the performance of the network through network slicing, in order that it can provide differentiated services to devices in the automated warehouse based on their needs, allowing network resources to be more efficiently allocated. With network optimization, low-latency and high reliability communication of the robot can be achieved in the automated warehouse. The performance of network slicing was compared to the scenario without this technology in the experiments. Specifically, in the standard Wireless Fidelity (Wi-Fi) network scenario without network slicing, all devices and robots will be connected to one channel to send data to the Multi-access Edge Computing (MEC) server. For the network with slicing, we divide it into three slices based on different use cases, including computers, Internet of Things (IoT) devices, and robots. Slices are created by defining multiple Service Set Identifiers (SSIDs) in a single Access Point (AP). Our results show that network slicing technology can significantly improve network performance in the automated warehouse. The network with slicing is superior to that without slicing in terms of latency at different levels of network load, which is reduced by up to 53.6%. The throughput is also increased by up to 33.5% compared to the network without slicing. Meanwhile, the network with slicing can maintain a relatively low error probability of all flows, of which the median value is 0%. It can prove that network slicing technology is beneficial for the automated warehouse network. / Begreppet samarbete mellan människa och robot (HRC) har blivit vanligt förekommande inom modern industri. I det tidigare arbetet presenteras en säkerhetsram som är utrustad med en MEC-server (Multi-access Edge Computing) för att tillhandahålla tillräcklig resurser till roboten som arbetar i det automatiserade lagret med HRC scenario. När antalet robotar i det automatiserade lagret ökar ökar, kommer nätverket att bli en flaskhals. En långsiktig, modern och robust nätverk för automatiserade lager är därför önskvärt för att anpassa sig till eventuella framtida behov. I det här projektet undersöks genomförandet av nätverksindelning i automatiserade lager med HRC-scenario. Målet är att förbättra prestanda för nätverket genom att dela upp nätverket så att det kan tillhandahålla differentierade tjänster till enheter i det automatiserade lagret baserat på utifrån deras behov, vilket gör att nätverksresurserna kan fördelas mer effektivt. Med nätverksoptimering kan kommunikation med låg latenstid och hög tillförlitlighet av roboten kan uppnås i det automatiserade lagret. Vi utförde experiment med två scenarier: standardscenarier med en Wireless Fidelity (Wi-Fi)-nätverk och Wi-Fi-nätverk med nätverksslicing. I standardscenariot för Wi-Fi-nätverk är alla enheter och robotar anslutna till en kanal för att skicka data till MEC-servern. För nätverket med slicing delar vi upp det i tre skivor baserat på olika användningsfall, inklusive datorer, IoT-enheter (Internet of Things) och robotar. Skivorna är skapas genom att definiera flera SSID:er (Service Set Identifiers) i ett enda åtkomstnät. punkt (AP). Våra resultat visar att tekniken för att dela upp nätverk kan förbättra följande avsevärt nätverksprestanda i det automatiserade lagret. Nätet med skivning är överlägset det utan skivning när det gäller latens på olika nivåer av nätverks belastning, som minskas med upp till 53,63 %. Nätet med skivning kan också fortfarande upprätthålla en relativt låg felsannolikhet för att säkerställa nätverkskvaliteten samtidigt som samtidigt som det ger hög genomströmning. Det visar att tekniken för nätverksskivning är fördelaktig för det automatiserade lagernätverket.