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).

No description available.

QC 20110427

In recent years, the main trend in wireless communications has been shifted from voice transmission to data-centric communication. This shift has caused an increase in the data rate requirements for future wireless communication systems. These requirements result in need for large bandwidth. Being a limited and thus expensive resource, wireless spectrum needs to be used efficiently. For higher spectral efficiency, new transmission techniques as well as new dynamic spectrum-allocationpolicies are needed. Cognitive radio is a promising approach for increasing spectral efficiency of wireless systems. By exploiting advanced signal processing techniques and sophisticated transmission schemes, cognitive radio devices allow to serve new wireless users within the existing crowded spectrum. Typically, a cognitive radio network is installed in parallel to an existing primary network, a legacy owner of the spectrum. The cognitive radio network adapts to its electro-magnetic environment in order to limit or even avoid the disturbance to the primary network. This thesis focuses on the underlay cognitive radio paradigm, which assumes that both the primary network and the ad hoc cognitive radio network operate within the same time and frequency band, as well as at the same geographic location. The cognitive network is able to estimate the interference caused to the primary network by means of channel training and possible feedback. This knowledge is then used to adjust the cognitive network’s transmissions in such a way that the disturbance to the primary network is below some acceptable threshold. In the first part of the thesis, we discuss the multi-hop line cognitive networks, in which the information content before reaching its destination passes through several hops from node to node within the cognitive network. In this way, transmission power at the source terminal may be decreased, thus producing less interference to the primary network. Moreover, the powers at each terminal within the cognitive network may be optimally allocated so that the interference constraint at the primary network is satisfied. This power allocation can be realized in both centralized and decentralized ways, depending on the available information about the channel state. We discuss both of these allocations subject to different interference constraints employed at the primary network. In the second part of the thesis, we discuss the reliability of transmission within the line cognitive ad hoc networks in terms of outage probability and diversity. We also illustrate the benefit of network coding for such networks and provide a heuristic algorithm for optimal scheduling. In the final part of the thesis, we study the uplink relay-assisted cellular cognitive radio scenario. Both, the cognitive network and the primary network, contain a set of multi-antenna users that communicate with a corresponding base station. The users create mutual interference and hence limit each other’s performance. Using certain mathematical tools originally developed within the field of statistical physics, we are able derive a closed-form expression for the ergodic mutual information for arbitrary channels inputs, which enables characterization of the achievable rate region of such scenario. / <p>QC 20120612</p>

The penetration of smart pocket-size devices that provide constant Internet connectivity, such as mobile phones, has significantly changed the way people obtain, view and share information. Content provision is not anymore a prerogative to professionals; individuals are not solely customers, but also act as content generators and distributors. This shift in social behavior requires changes in the way information is delivered to target audiences in an efficient, interest-based and location-aware manner. This thesis explores a solution for opportunistic content distribution in a content-centric network that primarily targets content dissemination among mobile users in urban areas. The term ’opportunistic’ here refers to a concept which rejects the assumption of always-connected user devices and instead allows nodes to leverage sporadic contacts which occur when two neighbors come into direct radio communication range. Such communication mode allows data exchanges to occur in areas with little or no infrastructure; moreover, it is a potential solution for offloading the increasing traffic volumes observed by mobile operators. The contributions of this thesis lie in three areas. We first outline a general architecture and design for opportunistic content-centric networking. We implement our proposal on the Google Android platform, and provide application scenarios which illustrate the potential of mobile peer-to-peer communication. Our tests however show that energy consumption turns out to be a major issue for opportunistic networks. Therefore, our second effort is in the area of energy-efficiency. We propose a dual-radio architecture for opportunistic communication, and evaluate it through extensive simulations on realistic human mobility traces. Our final study lies in the area of content dissemination when nodes in the network act altruistically and are willing to solicit data on behalf of other participants. We propose a number of relaying and caching strategies, and evaluate them through simulations in environments that exhibit different churn levels. / <p>QC 20120806</p>

Rapid mobile data traffic growth is becoming in a reality and several forecasts expect that it will be continued in upcoming years. It is expected that significant indoor investment will be made not only by traditional operators but also by facility owners for their own purposes. A key challenge to such local network providers is provisioning ever-increasing mobile traffic demand at the current level of production cost per bit. A popular deployment strategy so far is deploying WLAN networks. While denser indoor deployment is foreseen, the interference from inside of a network as well as other neighboring operators can be a limiting factor for higher capacity. Tighter interference management will certainly provide higher efficiency in network and spectrum usage. Nevertheless, costs to allow fast information sharing among access points are necessary for advanced interference coordination. Moreover, managing interference across networks owned by different operators raises not only infrastructure cost but also the network interrelatedness which operators are typically reluctant for business independency. When taking into account the cost and barriers for interference coordination, it is still not so obvious that coordination in wireless broadband systems will be advantageous to operators. In this thesis, we address the operator benefit of downlink interference coordination in two aspects: 1) multi-cell coordination with no interference from neighboring operators, and 2) inter-operator coordination in shared spectrum. In order to deal with interference and cost tradeoff analysis, we explicitly develop a techno-economic analysis framework and reform a traditional cost model. Numerical results indicate that the economic benefit of the multi-cell coordination significantly depends on propagation conditions and average user demand level. A self-deployed WLAN network can be the cheapest deployment option in closed areas up to certain average demand level. Over the demand level or in open areas, advanced joint processing schemes in a cellular domain may be a viable solution. The drawback is that it requires extremely accurate channel state information at transmitters for practical usage. When inter-operator interferences is present, asymmetric cellular networks will be likely to appear due to business independency and selfishly compete to access spectrum with no or little network-level coordination. A network designed for more fairness with higher transmission power will have more benefit against the other counterpart. Although asymmetric competition lets operators unfairly utilize spectrum, sharing spectrum with reasonable geographical separation can outperform over static coordination, i.e., traditional spectrum split. Tight cooperation to maximize a common objective can further offer the performance benefit to both involved partners. However, the cooperation gain quickly diminishes as network separation and size increases because self-interference becomes more dominant. / <p>QC 20121031</p> / The Mobile Broadband Project Phase 3: “More for less” (MBB++)