Korkean luotettavuuden verkkohallinteiset laitteiden väliset yhteydet

Fifth generation cellular networks aim to provide new types of services. Prominent amongst these are industrial automation and vehicle-to-vehicle communications. Such new use cases demand lower latencies and higher reliability along with greater flexibility than current and past generations of cellular technologies allow. Enabling these new service types requires the introduction of device-to-device communications (D2D). This work investigated network-controlled D2D schemes wherein cellular base stations retain control over spectrum usage. D2D nodes assemble into clusters. Each D2D cluster then organises itself as it sees fit within the constraints imposed by the cellular network. A review of proposed D2D control schemes was conducted to identify pertinent interference issues. Measurements were then devised to empirically collect quantitative data on the impact of this interference. Measurements were conducted using a software-defined radio (SDR) platform. An SDR based system was selected to enable a low cost and highly flexible iterative approach to development while still providing the accuracy of real-world measurement. D2D functionality was added to the chosen SDR system with the essential parts of Long Term Evolution Release 8 implemented. Two series of measurements were performed. The first aimed to determine the adjacent channel interference impact of a cellular user being located near a D2D receiver. The second measurement series collected data on the co-channel interference of spectrum re-use between a D2D link and a moving cellular transmitter. Based on these measurements it was determined that D2D communications within a cellular system is feasible. Furthermore, the required frequency of channel state information reporting as a function of node velocity was determined.Viidennen sukupolven solukkoverkoilla pyritään mahdollistamaan uudentyyppisiä palveluja kuten teollisuusautomatiikkaa ja ajoneuvojen välistä viestintää. Tämänkaltaiset uudet käyttötarkoitukset vaativat lyhyempien viiveiden ja korkeammat luotettavuuden ohella myös suurempaa joustavuutta kuin minkä nykyisen sukupolven matkapuhelinverkkoteknologiat sallivat. Edellä mainittujen uusien palvelujen toteuttaminen vaatii suoria laitteiden välisiä yhteyksiä (engl. D2D). Tässä diplomityössä keskityttiin tutkimaan verkkohallinteisia D2D-rakenteita, joissa solukkoverkko hallinnoi spektrin käyttöä. D2D-päätteet liittyvät yhteen muodostaakseen klustereita, jotka hallinnoivat sisäistä tietoliikennettään parhaaksi katsomallaan tavalla solukkoverkon asettamien rajoitusten puitteissa. Kirjallisuuskatsauksen avulla selvitettiin aiemmissa tutkimuksissa esitetyille D2D-ratkaisuille yhteiset interferenssiongelmat. Näiden vaikutusta ja suuruutta tutkittiin mittausten avulla. Mittaukset toteutettiin ohjelmistoradioalustan (engl. SDR) avulla. SDR-pohjaisen järjestelmän käyttö mahdollisti edullisen ja joustavan tavan kerätä empiirisiä mittaustuloksia. D2D-toiminnallisuus lisättiin Long Term Evolution Release 8:n olennaiset ominaisuudet omaavaan alustaan. Tällä alustalla toteutettiin kaksi mittaussarjaa. Ensimmäisellä kerättiin tuloksia viereisellä kanavalla toimivan matkapuhelimen D2D-vastaanottimelle aiheuttamasta interferenssistä näiden ollessa toistensa läheisyydessä. Toisella mittaussarjalla selvitettiin samalla kanavalla toimivan D2D-yhteyden ja liikkuvan matkapuhelimen välistä interferenssiä. Mittausten perusteella todettiin D2D-toiminnallisuuden lisäämisen solukkoverkkoon olevan mahdollista. Lisäksi laskettiin vaadittava kanavalaadun päivitystiheys päätteiden nopeuden funktiona

On the Integration of Unmanned Aerial Vehicles into Public Airspace

Unmanned Aerial Vehicles will soon be integrated in the airspace and start serving us in various capacities such as package delivery, surveillance, search and rescue missions, inspection of infrastructure, precision agriculture, and cinematography. In this thesis, motivated by the challenges this new era brings about, we design a layered architecture called Internet of Drones (IoD). In this architecture, we propose a structure for the traffic in the airspace as well as the interaction between the components of our system such as unmanned aerial vehicles and service providers. We envision the minimal features that need to be implemented in various layers of the architecture, both on the Unmanned Aerial Vehicle (UAV)'s side and on the service providers' side. We compare and contrast various approaches in three existing networks, namely the Internet, the cellular network, and the air traffic control network and discuss how they relate to IoD. As a tool to aid in enabling integration of drones in the airspace, we create a traffic flow model. This model will assign velocities to drones according to the traffic conditions in a stable way as well as help to study the formation of congestion in the airspace. We take the novel problem posed by the 3D nature of UAV flights as opposed to the 2D nature of road vehicles movements and create a fitting traffic flow model. In this model, instead of structuring our model in terms of roads and lanes as is customary for ground vehicles, we structure it in terms of channels, density and capacities. The congestion is formulated as the perceived density given the capacity and the velocity of vehicles will be set accordingly. This view removes the need for a lane changing model and its complexity which we believe should be abstracted away even for the ground vehicles as it is not fundamentally related to the longitudinal movements of vehicles. Our model uses a scalar capacity parameter and can exhibit both passing and blocking behaviors. Furthermore, our model can be solved analytically in the blocking regime and piece-wise analytically solved when in the passing regime. Finally, it is not possible to integrate UAVs into the airspace without some mechanism for coordination or in other words scheduling. We define a new scheduling problem in this regard that we call Vehicle Scheduling Problem (VSP). We prove NP-hardness for all the commonly used objective functions in the context of Job Shop Scheduling Problem (JSP). Then for the number of missed deadlines as our objective function, we give a Mixed Integer Programming (MIP) formulation of VSP. We design a heuristic algorithm and compare the quality of the schedules created for small instances with the exact solution to the MIP instance. For larger instances, these comparisons are made with a baseline algorithm

A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing

Spectrum Management using Markov Decision Processes

Abstract: The advent of cognitive radio technology has enabled dramatically more options in the use of RF spectrum, allowing multiple transmitters to effectively share spectrum in ways that were previously unavailable (either due to technical limitations or regulatory restrictions). In this dissertation, we investigate approaches to managing RF spectrum use, with a focus on combining multiple control decisions in a mutually beneficial manner. Our approach to making spectrum management decisions is grounded in Markov decision theory, which has a rich formal foundation and is frequently used to guide decision making in other disciplines. Here, we develop a set of Markov Decision Processes (MDPs) that model the RF spectrum management problem (in various forms). These MDPs are then queried to provide guidance for management decisions, including the combination of both admission and modulation decisions. This results in control decisions that are optimal in expectation. To address the computational complexity inherent in computing these control decisions, we develop heuristic approaches that mimic the MDP\u27s decisions based upon patterns observed in the MDP decision space. These heuristics are shown to closely approximate the optimal results from the MDP. Finally, we empirically assess the appropriateness of using Markov decision theory for RF spectrum management by comparing our MDPs to a discrete-event simulation model that relaxes several of the modeling assumptions made in the development of the MDPs

Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks

Decision support continuum paradigm for cardiovascular disease: Towards personalized predictive models

Clinical decision making is a ubiquitous and frequent task physicians make in their daily clinical practice. Conventionally, physicians adopt a cognitive predictive modelling process (i.e. knowledge and experience learnt from past lecture, research, literature, patients, etc.) for anticipating or ascertaining clinical problems based on clinical risk factors that they deemed to be most salient. However, with the inundation of health data and the confounding characteristics of diseases, more effective clinical prediction approaches are required to address these challenges. Approximately a few century ago, the first major transformation of medical practice took place as science-based approaches emerged with compelling results. Now, in the 21st century, new advances in science will once again transform healthcare. Data science has been postulated as an important component in this healthcare reform and has received escalating interests for its potential for ‘personalizing’ medicine. The key advantages of having personalized medicine include, but not limited to, (1) more effective methods for disease prevention, management and treatment, (2) improved accuracy for clinical diagnosis and prognosis, (3) provide patient-oriented personal health plan, and (4) cost containment. In view of the paramount importance of personalized predictive models, this thesis proposes 2 novel learning algorithms (i.e. an immune-inspired algorithm called the Evolutionary Data-Conscious Artificial Immune Recognition System, and a neural-inspired algorithm called the Artificial Neural Cell System for classification) and 3 continuum-based paradigms (i.e. biological, time and age continuum) for enhancing clinical prediction. Cardiovascular disease has been selected as the disease under investigation as it is an epidemic and major health concern in today’s world. We believe that our work has a meaningful and significant impact to the development of future healthcare system and we look forward to the wide adoption of advanced medical technologies by all care centres in the near future.Open Acces

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains