Network function virtualization is gaining acceptance as modern approach enabling telecom equipments to run as software modules known as VirtualNetwork Functions(VNFs) on IT hardware on top of cloud.To host these modules, virtual infrastructure is needed within the cloud.For this purpose,cloud orchestrators are used. These are cloud specific and usually one cloud or-chestrator may not be compatible with other clouds. Investigating on generic orchestrators which are compatible with any cloud platform will reduce complexity and provides a single approach for creating virtual infrastructure.Our goal is to investigate on how generic orchestrators can be used to deployVNFs on cloud. The detailed analysis of cloud agnostic orchestrators over cloud native orchestrators is done. Resources that are needed for a VNFare described in a template supported by generic orchestrators and compare it with template of cloud native orchestrator. Results are analyzed by verifying whether the orchestration engines, Cloudify and Terraform can use those templates to create various resources on cloud environment. We sum-mate that both orchestrators can be used for deploying VNFs on cloud. TheVNF description for Cloudify is based on TOSCA which is slightly complex compared to Terraform. Cloudify using TOSCA related syntax is becoming standard. Terraform though uses HCL syntax similar to JSON makes it simpler for VNF description. Same study can be done on other cloud platforms such as VMware. Keywords:Terraform, Cloudify, Virtual Network Function

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Location Based Services (LBS) calculate the position of the user for different purposes like advertising and navigation. Most importantly, these services are also used to help emergency services by calculating the position of the person that places the emergency phone call. This has introduced a number of requirements on the accuracy of the measurements of the position. Observed Time Difference of Arrival (OTDOA) is the method used to estimate the position of the user due to its high accuracy. Nevertheless, this method relies on the correct reception of so called positioning signals, and therefore the calculations can suffer from errors due to interference between the signals. To lower the probability of interference, muting patterns can be used. These methods can selectively mute certain signals to increase the signal to interference and noise ratio (SINR) of others and therefore the number of signals detected. In this thesis, a simulation environment for the comparison of the different muting patterns has been developed. The already existing muting patterns have been simulated and compared in terms of number of detected nodes and SINR values achieved. A new muting pattern has been proposed and compared to the others. The results obtained have been presented and an initial conclusion on which of the muting patterns offers the best performance has been drawn. / muting, patterns, location, networks, OTDOA, LTE, PCI

The purpose of this project was to design and manufacture a Low-Noise Amplifier (LNA) working at a 5 GHz frequency band, by using High Electron Mobility Transistor (HEMT) from Avago Technologies. To improve our design, it was necessary to build a two-stage amplifier; one stage to work in minimum noise sensitivity, and another stage to get the maximum gain achievable by the transistor. This thesis work was carried out as a part of the UAV (Unmanned Aerial Vehicle) system project developed by a research group at the Radio communication and Microwave Electronics department, UMH.The project was designed and simulated using Agilent ADS (Advanced Design System) software.

One of the main advantages of 5G is the use of high frequency signals and above 6GHz frequency range is one of the most researched topics in the wireless communication industry. Large unused spectrum is expected to be used in the next generation 5G communication. Also, there is a great possibility of using that high frequency band for the drone in an industrial environment. But with the high frequency comes higher path loss, so it is important to study the path loss model at different environment of unused frequencies. Contemporary research on 5G frequencies mainly focuses on the 28 GHz band, the 38 GHz band, the 60 GHz band, and the E-band (71–76 and 81–86 GHz). But This thesis focuses on designing a test system for 7,05 GHz frequency band and then the measurements were conducted. Which was conducted at different height of the receiver while the transmitter height was constant. Also, the distance between Transmitter and receiver was varied in the entire experimental procedure. The measurements were performed at Research Laboratory at University of Gävle. Omni directional antennas were used, and co-polarization and cross-polarization antenna configuration were used to measure the received power. The measurement provided a statistical overview and nature of received power at 7,05 GHz at different heights of receiver. Additionally, different materials were placed between Transmitter and Receiver, to see the Effects of these on the received power in different conditions before and after placing the materials in between transmitter and receiver.

· Purpose Artificial Intelligence is one of the trend areas in research. It is applied in many different contexts successfully including Telecom sector. The purpose of this study is to replicate the study done in application of AI in the medical sector to understand the similar challenges of using AI in the Telecom sector. · Design/Methodology/approach Online questionnaire-based empirical study is used, and 190 responses were collected. First authors compare the general Technology acceptance model framework used in the medical sector and compare it with the non-AI users. Afterwards, this study proposes the improved TAM model that best fits into the Telecom sector. Later, this study uses the proposed improved model to compare the AI and non-AI users to understand the acceptance of AI-technology tools application in the Telecom sector. · Findings Confirmatory Factor analysis revealed that the general TAM model fit is adequate and applicable in Medical sector as well as in the Telecom sector. Also, hypothesis testing using SEM concluded that the general supported paths between the constructs and variables related to PU, PEU, SN, ATU, and BI in the medical sector is not same as in the Telecom sector. · Research limitations Results are based on the limited datasets from one of the larger companies in Telecom sector which could leads to inherent biases. Authors not sure if “AI-technology tools” in the questions have common understanding across all the respondents or not. · Results TAM model cannot be generalized across the sectors. An improved model has been developed used in the Telecom sector to analyze the user’s behavior and acceptance of AI technology. An extended model has been proposed which can be used as a continuation of this study. Keywords: Medical, Telecom, Artificial Intelligence, Network Intelligence, Technology acceptance model (TAM), Confirmatory Factor analysis (CFA), Structural equation modeling (SEM), Perceived usefulness (PU), Perceived Ease of Use (PEU), Subjective Norms (SN), Attitude Towards AI Use (ATU), Behavioural Intention (BI).

Computational electromagnetic (CEM) simulations are an important tool in the deployment and development of wireless communications. By utilising a MATLAB-based CEM simulator this thesis aims to develop and expand the simulation toolboxes for visualising radio path propagation with a focus on indoor environment and using new technologies such as virtual reality (VR). The vision is to be able to find new ways of exploring radio deployments to find their strengths and weaknesses in different environments. The goal of the work is to create a visualisation application using Unreal Engine that can interface with the MATLAB-based CEM simulator. The developed application can interpret spatial simulation-domains, ray-tracing/path propagation data, and scalar fields. In addition, the application can construct 3D-worlds from the spatial data and display radio path propagation in VR. Values for scalar fields can be viewed when selecting specific parameters, however, potential improvements will allow for the visualisation of entire scalar fields. There are many opportunities for future work from this thesis, both immediate small improvements and larger feature additions.

Our thesis aims to develop and implement an AI-based model for face recognition using the Docker container, such that it can be transferable to any IoT platform. The main objective of the thesis is to develop an AI-based face recognition Model (which is implemented following the Deep Learning algorithm)for the security system for making decisions to lock or unlock the door system and to deploy the developed AI Model in a Docker Container on an IoT platform. The main aim of the thesis would be to achieve the edge computing concept that brings the Artificial Intelligence (through our AI model) to the low power Internet of Things (IoT) devices with the help of containerization concept. Containerisation would be similar to the virtualisation. Docker containers are easy to port on various IoT devices (Firefly rk3399). Along with the portability, Docker includes all the dependencies and modules required for running the application in a container. Our research work comprises the methodology of developing the containerised AI model. We have chosen the method of training the algorithm such that it detects the faces captured by our camera, which is connected with the help of CSI connector. The algorithm includes the concept of Deep Learning which is a subset of Artificial Intelligence. The method consists of several steps, for example, Deep learning Algorithm detects the faces from the image, and then the image is converted to a set of gradients. These gradients can be converted again to landmarks to consider the focal points of the image and then the training step is performed using the Support Vector Machine classifier. Finally, the authorised user is recognised. Our research work comprises the methodology of developing the containerised AI model and deploying the containerised application on the Raspberry Pi (IoT device), which consists of the ARM processor. We conclude that the containerised application run with high efficiency, is portable and transferable between multiple platforms, and the containerised application is compatible with multiple architectures (ARM, x86, amd64).

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