Transparent Elastic Optical Networks (EON) are seen as a promising solution for future optical transport networks to keep up with internet traffic growth, as they allow provisioning connections with different bandwidth requirements in an efficient way. To achieve high spectrum efficiency in these networks, making good Routing, Modulation and Spectrum Assignment (RMLSA) decisions is essential. Since fiber cuts are common, resiliency against single-link failures is another important topic. This can be provided efficiently through shared-path protection (SPP), which in turn complicates the RMLSA problem. Existing routing, modulation and spectrum assignment algorithms for SPP focus on the two-step approach, where primary paths are selected independently of their backup path options. However, selecting a different primary path can allow for a better backup path with higher shareability of backup resources if primary and backup path pairs are considered together. Previous studies on SPP in EONs mostly consider the static traffic scenario. Under a dynamic traffic scenario, where unpredictable connection re-quests arrive and terminate over time, fragmentation of spectral resources has a significant impact on the network performance. In this thesis, a new algorithm is proposed for SPP in EONs against single-link failures where primary and backup path pairs are selected jointly, thereby minimizing fragmentation and maximizing shareability which leads to better network performance in terms of blocking probability. Unlike existing algorithms, the primary and backup path and spectrum are decided simultaneously from a set of candidate path pairs and the spectrum assignment is done using a hybrid cost metric. The metric is a weighted combination of existing metrics that integrates fragmentation and shareability into a multi-objective function. Using network traffic simulations in two reference networks, the effect of the different cost functions on the algorithm’s behavior is explored and an optimal set of weights is determined. With this parameterization, traffic simulations in a scaled-down sample US network topology with load values of 190-240 Erlang, corresponding to blocking probabilities of 0.1% to 1%, show an average improvement over the reference algorithm of 79% in blocking probability, 6.9% in shareability and 5.9% in spectrum fragmentation. It is also shown that most of this improvement is caused by joint primary-backup path assignments. The hybrid cost function reduces blocking by a further 10%. / Transparenta Elastiska Optiska Nätverk (EON) ses som en lovande lösning för framtida optiska transportnät för att hänga med Internettrafikens tillväxt, eftersom de möjliggör att tillhandahålla förbindelser med olika krav på bandbredd på ett effektivt sätt. För att uppnå hög spektrumeffektivitet i dessa nätverk är det viktigt att fatta bra beslut vad avser routing, modulering och spektrumtilldelning (Routing, Modulation Level and Spectrum Assignment, RMLSA). Eftersom fiberavbrott är vanliga, så är tåligheten mot enstaka länkfel et annat viktigt ämne. Detta kan ske effektivt genom att skydda gemensamma reservvägar (Shared Path Protection, SPP), vilket dock försvårar RMLSA-problemet. Befintliga routing, modulering och spektrumtilldelningsalgoritmer för SPP fokuserar på strategier i två steg, där först de primära vägarna väljs oberoende av deras backupalternativ. Att välja en annan primär väg, kan dock möjliggöra en bättre reservväg med bättre delning av backupresurser om i stället par av primära och sekundära vägar bedöms tillsammans. Tidigare studier på SPP i EONs anser främst statiska trafikscenarier. I ett dynamiskt trafikscenario, där oförutsägbara anslutningsbegäranden inkommer och avslutas över tiden, så kommer fragmenteringen av spektrala resurser ha en betydande inverkan på nätverkets prestanda. I denna avhandling föreslås en ny algoritm för SPP i EONs för hantering av enskilda länkfel, där par av primära och sekundära vägar väljs gemensamt, vilket minimerar fragmentering och maximerar delning vilket leder till bättre nätverksprestanda i form av minskat blockering. Till skillnad från befintliga algoritmer beslutas den primära och sekundära vägen och spektrum samtidigt från en uppsättning av par av kandidatvägar och spektrumtilldelningen görs med en hybrid-kostnadsfunktion. Funktionen är en viktad kombination av befintliga mått som integrerar fragmentering och delning till en multi-objektiv målfunktion. Med användning av nätverkstrafiksimuleringar i två referensnätverk studeras effekten av olika kostnadsfunktioner på algoritmens beteende och en optimal uppsättning av vikter bestäms. Med dessa parametrar, trafiksimuleringar i en reducerad US-nätverkstopologi med belastningsvärden på 190-240 Erlang, motsvarande blockeringssannolikheter på 0,1 - 1%, visar en genomsnittlig förbättring under referensalgoritmen på 79% i blockeringens sannolikhet, 6.9% i delning och 5,9% i fragmentering. Det visas också att det mesta av denna förbättring beror på det samtidiga tilldelning av primära och reservlänkar. Hybridkostnadsfunktionen minskar blockeringen med ytterligare 10%.

The ever increasing demand for video content directed the focus of researchfrom traditional server-based schemes to peer-to-peer systems for videodelivery. In such systems, video data is delivered to the users by utilizing theresources of the users themselves, leading to a potentially scalable solution.Users connect to each other, forming a p2p overlay network on top of theInternet and exchange the video segments among themselves. The performanceof a p2p system is characterized by its capability to deliver the videocontent to all peers without errors and with the smallest possible delay. Thisconstitutes a challenge since peers dynamically join and leave the overlay andalso contribute different amounts of resources to the system.The contribution of this thesis lies in two areas. The first area is theperformance evaluation of the most prominent p2p streaming architectures.We study the streaming quality in multiple-tree-based systems. We derivemodels to evaluate the stability of a multiple tree overlay in dynamic scenariosand the efficiency of the data distribution over the multiple trees. Then, westudy the data propagation in mesh-based overlays. We develop a generalframework for the evaluation of forwarding algorithms in such overlays anduse this framework to evaluate the performance of four different algorithms.The second area of the thesis is a study of streaming in heterogeneous p2poverlays. The streaming quality depends on the aggregate resources that peerscontribute to the system: low average contribution leads to low streamingquality. Therefore, maintaining high streaming quality requires mechanismsthat either prohibit non-contributing peers or encourage contribution. In thisthesis we investigate both approaches. For the former, we derive a model tocapture the evolution of available capacity in an overlay and propose simpleadmission control mechanisms to avoid capacity drainage. For the latter, inour last work, we propose a novel incentive mechanism that maximizes thestreaming quality in an overlay by encouraging highly contributing peers tooffer more of their resources. / QC 20100506

In this thesis, periodic structures with higher symmetries symmetries are discussed. The main focus of the thesis is on twist symmetries. Importantly, the attractive properties of twist symmetries for the control of electromagnetic wave propagation are demonstrated. Furthermore, the additional degree of freedom offered by twist symmetries is used to design two microwave devices. A structure is twist-symmetric if its periodicity can be described by the geometrical operation consisting of a translation and a rotation around an axis. In this thesis, it is demonstrated that there are no stop-bands between the first q modes in the Brillouin diagram of a twist-symmetric structure, where q is the symmetry order. The importance of the symmetry to the absence or presence of stop-bands is illustrated by studying structures where the symmetry is gradually broken. Furthermore, it is demonstrated that a twist-symmetric structure can produce a higher and less dispersive effective refractive index, compared to a conventional periodic structure. These characteristics are attractive for the design of microwave devices. To provide insight into the physics of twist symmetry, a mode matching formulation is derived to analyze twist-symmetric coaxial transmission lines. The formulation is used to highlight the importance of higher order coupling on the response in structures with varying order of the twist symmetry.  In this thesis, we also discuss another type of symmetry; polar glide symmetry. A periodic structure possesses a polar glide symmetry if its periodicity can be described by the geometrical operation consisting of a translation and a reflection in a cylindrical surface. It is demonstrated that there is no stop-band between the first two modes in a polar glide structure, which is similar to what has been reported for Cartesian glide symmetry previously. Furthermore, twist symmetry and polar glide symmetry is combined into twisted polar glide symmetry. The effect of this combination on the stop-band between the second and third modes is demonstrated. It is concluded that this type of symmetry finds application in filter design. Finally, the additional design freedom offered by twist symmetry is used to produce two microwave components. A reconfigurable phase shifter is designed, where the phase delay in the phase shifter depends on the order of the twist symmetry. Furthermore, a flat lens is designed to transform a spherical wave into a quasi-planar wave. The focusing properties of the lens is obtained by locally varying the order of the symmetry throughout the lens aperture. / I denna avhandling diskuteras periodiska strukturer med högre symmetrier. Huvudfokus av avhandlingen är skruvsymmetrier. Vi visar de attraktiva egenskaperna av skruvsymmetrier för kontrollen av elektromagnetiska vågor. Dessutom används den extra designfriheten från skruvsymmetrier för att designa två mikrovågskomponenter. En struktur är skruvsymmetrisk om dess period kan beskrivas av en geometriska operation bestående av en translation och en rotation runt en axel. I denna avhandling visar vi att det inte finns några bandgap mellan de första q moderna i Brillouindiagrammet för en skruvsymmetrisk struktur, där q är symmetriordningern. Betydelsen av symmetri för frånvaron och närvaron av bandgap illustreras genom att studera strukturer där symmetrin gradvis bryts. Vidare visar vi att en skruvsymmetrisk struktur kan producera ett högre och mindre dispersivt effektivt refraktivt index jämfört med en vanlig periodisk struktur. Dessa egenskaper är attraktiva i designen av mikrovågskomponenter. För att ge insikt i fysiken bakom skruvsymmetrier tas en modmatchningsformulering fram för att studera skruvsymmetriska koaxiala transmissionsledare. Formuleringen används för att belysa vikten av högre ordnings vågkoppling för egenskaperena i strukturer med olika ordnings skruvsymmetri.  I denna avhandling diskuteras ytterliage en typ av högre symmetri; polär glidsymmetri. En struktur har en polär glidsymmetri om dess period kan beskrivas av en geometriska operation bestående av en translation och en spegling i en cylindrisk yta. Vi visar att det inte finns något bandgap mellan de två första moderna i en struktur med polär glidsymmetri, vilket också tidigare visats för Cartesisk glidsymmetri. Vidare kombinerar vi skruv- och polär glidsymmetri. Implikationerna av denna kombination på bandgapet mellan högre moder diskuteras. Vi kommer fram till att denna typ av kombinerad symmetri finner tillämpningar i filterdesign.  Slutligen används den extra designfriheten från skruvsymmetri för att designa två mikrovågskomponenter. En konfigurerbar fasskiftare designas, där fasskiftningen beror på symmetriordningen. Vidare designas en platt lins som omvandlar en sfärisk våg till en kvasiplan våg. Fokuseringsegenskaperna i linsen uppnås genom att lokalt variera symmetriordningen i linsens apertur. / <p>QC 20211103</p>

In this thesis, the problem of finding an optimal training sequence for estimating a MIMO flat fading channel with spatially and temporally correlated Gaussian noise is considered. The methods analyzed tailor the training sequence not only according to the known statistical CSI but also to the specific purpose the channel estimate will fulfill. The task of obtaining the optimal training sequence is formulated in two different ways, either guaranteeing a specific performance or setting a maximum training power budget. Two different applications are considered, the ZF precoder and the MMSE equalizer. The performance of the training sequence obtained by minimizing a metric that is representative for this applications is compared to using the training sequence that minimizes the channel estimate mean square error. Additionally, since some approximations are required to solve the optimization problem when using the application-oriented metrics, the impact of these is analyzed. Two different approximations that allow convexification and lead to SDP formulations are considered for each problem. The two approximations and problem formulations are analyzed in terms of performance, training power and outage probability. The SDP formulations are then compared to the solutions provided by builtin functions in MATLAB that converge to a minimum in order to obtain information about how far from optimal the solutions obtained from the SDP are.

Estimating an object’s position can be of great interest in several applications,and there exists many different methods to do so. One approach is with Directionof Arrival (DOA) measurements from receivers to use the triangulation techniqueto estimate one or more transmitter’s position. One algorithm which can find theDOA measurements from several transmitters is the MUltiple SIgnal Classification(MUSIC) algorithm. However, this still leaves a ambiguity problem which givesfalse solutions, so called ghost points, if the number of receivers is not sufficient.In this report solving this problem with the help of deep learning is studied. Thethesis’s main objective is to investigate and study whether it is possible to performpositioning with measurements from the MUSIC-algorithm using deep learningand image processing methods. A deep neural network is built in TensorFlow and trained and tested using datagenerated from MATLAB. This thesis’s setup consists of two receivers, which areused to locate two transmitters. The network uses two MUSIC spectra from thetwo receivers, and returns a probability distribution of where the transmittersare located. The results are compared with a traditional method and are analysed.The results presented in this thesis show that it is possible to perform positioningusing deep learning methods. However, there is a lot of room for improvementwith accuracy, which can be an important future research direction to explore.

Antennas are essential components in any wireless communication system. To evaluate them is challenging, especially when new technologies are emerging. Future intelligent transport systems, where vehicular communications play an important role will cover important aspects such as traffic safety and traffic efficiency. These applications will be covered by technologies such as IEEE 802.11p and LTE. For these emerging technologies, traditional methods for measuring the vehicular antennas such as anechoic chamber measurements or expensive and time-consuming field measurements may not be enough or suitable. Thus a new method for evaluating the antennas performance is desirable. A method that includes the multipath environment to give an idea of the antenna performance in the whole system and at the same time be able to be applied at early stages of product development. This thesis aims to provide such method. The thesis is divided in two parts. The first part contains an overview and background of important concepts needed for development of methods for evaluation of vehicular antennas. In the second part, the papers that constitute the core of this work are appended. In Paper A, we evaluate the vehicle’s antenna performance using only simulations. We start by defining the multipath environment for vehicle-tovehicle and vehicle-to-infrastructure (V2X) communication. Then, the V2X environment is simulated using a multipath simulation tool to evaluate the vehicle’s antennas radiation patterns placed at different positions on the vehicle. This will result in the received power cumulative distribution functions (CDFs) for the voltage samples at the receiving antennas port. In Paper B, we present the design and evaluation of an antenna module for IEEE 802.11p and LTE technologies. The module is designed taking into consideration the available space and suitable placement on the vehicle. The proposed module is in accordance with the requirements for LTE and IEEE 802.11p technologies. This is validated with the analysis of the antenna efficiencies, S-parameters, radiation patterns, and diversity performance for the simulated and measured antenna module. Finally, Paper C presents a method for the evaluation of V2V antennas in a simulated measurement-based multipath environment. Here, a measurement campaign is performed to obtain the parameters (i.e., the angular received power spectrum) that define a realistic V2V multipath environment. These parameters are then introduced in a multipath simulation tool where the antennas radiation patterns are evaluated. Results are expressed in terms of received power CDFs. This method is validated by comparing the simulated and measured received power for two roof-top vehicle antennas.

The outbreak of COVID-19 is one of the major pandemics faced by the world ever and the World Health Organization (WHO) had declared it as the deadliest virus outbreak in recent times. Due to its incubation period, predicting or identifying the paints had become a tough job and thus, the impact is on a large scale. Most of the countries were affected with Coronavirus since December 2019 and the spread is still counting. Irrespective of the preventive measures being promoted on various media, still the speculations and rumors about this outbreak are peaks, that too particular with the social media platforms like Facebook and Twitter. Millions of posts or tweets are being posted on social media via various apps and due to this, the accuracy of news has become unpredictable, and further, it has increased panic among the people. To overcome these issues, a clear classification or categorization of the posts or tweets should be done to identify the accuracy of the news and this can be done by using the basic sentiment analysis technique of data sciences and machine learning. In this project, Twitter will be considered as the social media platform and the millions of tweets will be analyzed for aspect mining to categorize them into positive, negative, and neutral tweets using the NLP techniques. SVM and Naive Bayes approach of machine learning and this model will be developed.

In a telecommunications network locating user equipment (paging) is a common procedure. Proposed functionality for 4G and 5G allows for eNB initiated paging via X2 interfaces. In this thesis machine learning algorithms were evaluated in order to reduce page signalling. Additionally, two paging schemes based on machine learning were proposed and compared to a common method of paging through cost models. The results show that signalling cost can be reduced by up to 80%.

Low-Power Lossy Networks (LLNs) are a type of Internet of Things (IoT) meshnetwork that collaboratively interact and perform various tasks autonomously. TheRouting Protocol for Low-power and Lossy Network (RPL) is the most used rout-ing protocol for LLNs. Recently, we have been witnessing a tremendous increasein attacks on Internet infrastructures using IoT devices as a botnet (IoT botnet).This thesis focuses on two parts: designing an ML-based IDS for 6LoWPAN, andgenerating a new larger labeled RPL attack dataset by implementing various non-attack and attack IoT network scenarios in the Cooja simulator. The collected rawdata from simulations is preprocessed and labeled to train the Machine Learningmodel for Intrusion Detection System (IDS). We used Deep Neural Network (DNN),Random Forest Classifier (RFC), and Support Vector Machines with Radial-BasisFunction kernel (SVM-RBF) learning algorithms to detect attack in RPL based IoTmesh networks. We achieved a high accuracy (96.7%) and precision (95.7%) usingthe RFC model. The thesis also reviewed the possible placement strategy of IDSfrom cloud to edge.

LoRaWAN is one of the most adopted low-power wide-area networks, which use theproprietary LoRa modulation developed by Semtech to provide high sensitivity at lowdata rates for long ranges. LoRaWAN providing coverage using single-hop gatewaysto thousands of end-devices (sensors) running on batteries. Each gateway requires aconnection to the internet or a private network (Ethernet, 3G/4G, Wifi) to communicatewith the network server. In obstructed areas, with mountains, foliage, or simplyvast areas, multiple gateways are required to provide coverage for the end-devices.Internet/mobile coverage might be missing in some areas. Which quickly brings upthe cost of internet connection in these areas, unless a solution is developed that routesmessages between the gateways using the existing LoRa radio communication standard.Thus reducing required internet connections while providing coverage. This solutionwas developed and evaluated, with a focus on supporting LoRaWAN deviceswith bidirectional capabilities, following the regional regulations, and providing arouting protocol that minimizes the extra data that comes with routing messages overa network. Experiments of three gateways showed that the solution provides a functioningrouting protocol, with as low as 3 extra bytes for routing. While fast enough toprovide bidirectional communication support for end-devices located farthest awayfrom the gateway with the server connection.