Radio Resource Management across UMTS Radio Network Subsystems

UMTS (Universal Mobile Telecommunication System), esimerkkinä kolmannen sukupolven matkapuhelinjärjestelmästä pyrkii toistamaan GSM:n (Global System for Mobile Communications) menestyksen. UMTS:n kaupallinen toiminta on parhaillaan alkamassa ja ensimmäinen kaupallinen verkko on jo aloittanut toimintansa Japanissa. Tämä diplomityö antaa yleiskuvan UMTS:stä keskittyen radioverkkojärjestelmän (UMTS Terrestrial Radio Access Network,UTRAN) radioresurssien hallintaan (Radio Resource Management, RRM). Työssä kuvataan radiorajapintojen toimintaa, mutta diplomityön pääaiheena on kuitenkin radioresurssien hallinta UMTS radioaliverkkojärjestelmien ylitse. Radioresurssien hallinta pitää sisällään joukon proseduureja, jotka vaikuttavat koko UTRAN:in rakenteen lävitse. On hyvin tärkeää saavuttaa oikea toiminnallisuus hajautettujen radioresurssien hallintaan jotta voitaisiin saavuttaa paras yhteyden laatu loppukäyttäjälle. Työssä käydään yksityiskohtaisesti lävitse radioresurssien hallinnan perusperiaatteet ja joukko proseduureja. RNSAP (Radio Network Subsystem Application Part) protokollaa tarkastellaan työssä esimerkkinä protokollasta joka osallistuu radioresurssien hallintaprosessiin.

Transport Resource Management within UMTS Radio Network Subsystem

Tällä hetkellä haastavin telekommunikaatioteollisuuden tutkimus – ja kehitystoiminta on keskittynyt kolmannen sukupolven matkapuhelinjärjestelmien ympärille. Järjestelmien standardointityössä on saatu aikaiseksi ensimmäiset vakaat spesifikaatioversiot ja kaupallista toimintaa ollaan parhaillaan aloittelemassa Japanissa ja Euroopassa. Eräs kolmannen sukupolven järjestelmistä on UMTS (Universal Mobile Telecommunications System). Tämä diplomityö antaa yleiskuvan UMTS järjestelmästä ja sen eri verkkoelementtien toiminnallisuuksista. Päähuomio on kiinnitetty radioverkkojärjestelmään (UMTS Terrestrial Radio Access Network) ja erityisesti sen radioaliverkkojärjestelmään (Radio Network Subsystem), joka koostuu radioverkonohjaimesta (Radio Network Controller) ja joukosta siihen kuuluvia tukiasemia (Node B). Radioverkonohjain ja tukiasemat on yhdistetty avoimen rajapinnan kautta jota kutsutaan Iub -rajapinnaksi. Rajapinta tarjoaa radioverkonohjaimelle mahdollisuuden kontrolloida tukiasemia signalointiviestien avulla ja mahdollistaa tehokkaan ja luotettavan käyttäjätiedon siirron radioaliverkkojärjestelmän sisällä. Tämän diplomityön pääasiallinen sisältö on siirtoresurssien hallinta Iub -rajapinnan ylitse. Työssä esitellään ja selitetään siirtoverkon arkkitehtuuri. Myös kaikki Iub:ssä sijaitsevat protokollat ja toiminnalliset yksiköt jotka vaikuttavat siirtoresurssien hallintaan esitellään ja kuvataan yksityiskohtaisesti. Päähuomio on kiinnitetty sovellusprotokolliin sekä rajapinnan siirtoverkko- että radioverkkokerroksella sekä näiden protokollien väliseen vuorovaikutukseen. Kyseiset protokollat ovat Node B Application Part (NBAP) ja Access Link Control Application Part (ALCAP). Työn toteutusosassa käydään lävitse NBAP –protokollan prototyypin ja Node B Manager –toiminnallisen yksikön prototyypin implementaatio.

Success Factors of an Enterprise Resource Planning System

Työn tarkoituksena oli tutkia yrityksen eri yksiköiden toiminnaohjausjärjestelmiä sekä verrata niiden menestystekijöitä Adelakunin malliin tietojärjestelmien laatuulottuvuuksista. Siinä järjestelmän kokonaislaatu jaetaan liiketoiminnalliseen, tekniseen ja käyttäjän kokemaan laatuun. Tulosten perusteella oli myös tavoitteena kehittää kyseisen toiminnanojausjärjestelmän kehittämistä varten malli onnistumistekijöiden keskinäisestä riippuvuudesta. Tutkittavista järjestelmistä ja niiden käytöstä kerättiin tietoja käyttöönottoprojektien dokumentaatiosta, haastatteluin, kyselylomakkein ja järjestelmäanalyysein. Sekä loppukäyttäjät että yritysjohto olivat kyselyjen ja haastattelujen kohderyhmänä. Saatuja tietoja arvioitiin Adelakunin kolmiulotteisen tietojärjestelmän laatutekijämallin mukaisesti ja keskeisiä menestystekijöitä etsittiin. Tutkituissa tapauksissa tietojärjestelmien menestyksen taustalta löytyi alan kirjallisuuden kanssa yhtäpitäviä tekijöitä. Myös Adelakunin laatu-ulottuvuusmalli osoittautui validiksi tutkituissa tapauksissa. Keskeisten menestystekijöiden välisistä vuorovaikutussuhteista rakennettiin malli, jota voidaan hyödyntää kyseisen järjestelmän jatkokehityksessä.

Error models in hydrogeology applications

Notre consommation en eau souterraine, en particulier comme eau potable ou pour l'irrigation, a considérablement augmenté au cours des années. De nombreux problèmes font alors leur apparition, allant de la prospection de nouvelles ressources à la remédiation des aquifères pollués. Indépendamment du problème hydrogéologique considéré, le principal défi reste la caractérisation des propriétés du sous-sol. Une approche stochastique est alors nécessaire afin de représenter cette incertitude en considérant de multiples scénarios géologiques et en générant un grand nombre de réalisations géostatistiques. Nous rencontrons alors la principale limitation de ces approches qui est le coût de calcul dû à la simulation des processus d'écoulements complexes pour chacune de ces réalisations. Dans la première partie de la thèse, ce problème est investigué dans le contexte de propagation de l'incertitude, oú un ensemble de réalisations est identifié comme représentant les propriétés du sous-sol. Afin de propager cette incertitude à la quantité d'intérêt tout en limitant le coût de calcul, les méthodes actuelles font appel à des modèles d'écoulement approximés. Cela permet l'identification d'un sous-ensemble de réalisations représentant la variabilité de l'ensemble initial. Le modèle complexe d'écoulement est alors évalué uniquement pour ce sousensemble, et, sur la base de ces réponses complexes, l'inférence est faite. Notre objectif est d'améliorer la performance de cette approche en utilisant toute l'information à disposition. Pour cela, le sous-ensemble de réponses approximées et exactes est utilisé afin de construire un modèle d'erreur, qui sert ensuite à corriger le reste des réponses approximées et prédire la réponse du modèle complexe. Cette méthode permet de maximiser l'utilisation de l'information à disposition sans augmentation perceptible du temps de calcul. La propagation de l'incertitude est alors plus précise et plus robuste. La stratégie explorée dans le premier chapitre consiste à apprendre d'un sous-ensemble de réalisations la relation entre les modèles d'écoulement approximé et complexe. Dans la seconde partie de la thèse, cette méthodologie est formalisée mathématiquement en introduisant un modèle de régression entre les réponses fonctionnelles. Comme ce problème est mal posé, il est nécessaire d'en réduire la dimensionnalité. Dans cette optique, l'innovation du travail présenté provient de l'utilisation de l'analyse en composantes principales fonctionnelles (ACPF), qui non seulement effectue la réduction de dimensionnalités tout en maximisant l'information retenue, mais permet aussi de diagnostiquer la qualité du modèle d'erreur dans cet espace fonctionnel. La méthodologie proposée est appliquée à un problème de pollution par une phase liquide nonaqueuse et les résultats obtenus montrent que le modèle d'erreur permet une forte réduction du temps de calcul tout en estimant correctement l'incertitude. De plus, pour chaque réponse approximée, une prédiction de la réponse complexe est fournie par le modèle d'erreur. Le concept de modèle d'erreur fonctionnel est donc pertinent pour la propagation de l'incertitude, mais aussi pour les problèmes d'inférence bayésienne. Les méthodes de Monte Carlo par chaîne de Markov (MCMC) sont les algorithmes les plus communément utilisés afin de générer des réalisations géostatistiques en accord avec les observations. Cependant, ces méthodes souffrent d'un taux d'acceptation très bas pour les problèmes de grande dimensionnalité, résultant en un grand nombre de simulations d'écoulement gaspillées. Une approche en deux temps, le "MCMC en deux étapes", a été introduite afin d'éviter les simulations du modèle complexe inutiles par une évaluation préliminaire de la réalisation. Dans la troisième partie de la thèse, le modèle d'écoulement approximé couplé à un modèle d'erreur sert d'évaluation préliminaire pour le "MCMC en deux étapes". Nous démontrons une augmentation du taux d'acceptation par un facteur de 1.5 à 3 en comparaison avec une implémentation classique de MCMC. Une question reste sans réponse : comment choisir la taille de l'ensemble d'entrainement et comment identifier les réalisations permettant d'optimiser la construction du modèle d'erreur. Cela requiert une stratégie itérative afin que, à chaque nouvelle simulation d'écoulement, le modèle d'erreur soit amélioré en incorporant les nouvelles informations. Ceci est développé dans la quatrième partie de la thèse, oú cette méthodologie est appliquée à un problème d'intrusion saline dans un aquifère côtier. -- Our consumption of groundwater, in particular as drinking water and for irrigation, has considerably increased over the years and groundwater is becoming an increasingly scarce and endangered resource. Nofadays, we are facing many problems ranging from water prospection to sustainable management and remediation of polluted aquifers. Independently of the hydrogeological problem, the main challenge remains dealing with the incomplete knofledge of the underground properties. Stochastic approaches have been developed to represent this uncertainty by considering multiple geological scenarios and generating a large number of realizations. The main limitation of this approach is the computational cost associated with performing complex of simulations in each realization. In the first part of the thesis, we explore this issue in the context of uncertainty propagation, where an ensemble of geostatistical realizations is identified as representative of the subsurface uncertainty. To propagate this lack of knofledge to the quantity of interest (e.g., the concentration of pollutant in extracted water), it is necessary to evaluate the of response of each realization. Due to computational constraints, state-of-the-art methods make use of approximate of simulation, to identify a subset of realizations that represents the variability of the ensemble. The complex and computationally heavy of model is then run for this subset based on which inference is made. Our objective is to increase the performance of this approach by using all of the available information and not solely the subset of exact responses. Two error models are proposed to correct the approximate responses follofing a machine learning approach. For the subset identified by a classical approach (here the distance kernel method) both the approximate and the exact responses are knofn. This information is used to construct an error model and correct the ensemble of approximate responses to predict the "expected" responses of the exact model. The proposed methodology makes use of all the available information without perceptible additional computational costs and leads to an increase in accuracy and robustness of the uncertainty propagation. The strategy explored in the first chapter consists in learning from a subset of realizations the relationship between proxy and exact curves. In the second part of this thesis, the strategy is formalized in a rigorous mathematical framework by defining a regression model between functions. As this problem is ill-posed, it is necessary to reduce its dimensionality. The novelty of the work comes from the use of functional principal component analysis (FPCA), which not only performs the dimensionality reduction while maximizing the retained information, but also allofs a diagnostic of the quality of the error model in the functional space. The proposed methodology is applied to a pollution problem by a non-aqueous phase-liquid. The error model allofs a strong reduction of the computational cost while providing a good estimate of the uncertainty. The individual correction of the proxy response by the error model leads to an excellent prediction of the exact response, opening the door to many applications. The concept of functional error model is useful not only in the context of uncertainty propagation, but also, and maybe even more so, to perform Bayesian inference. Monte Carlo Markov Chain (MCMC) algorithms are the most common choice to ensure that the generated realizations are sampled in accordance with the observations. Hofever, this approach suffers from lof acceptance rate in high dimensional problems, resulting in a large number of wasted of simulations. This led to the introduction of two-stage MCMC, where the computational cost is decreased by avoiding unnecessary simulation of the exact of thanks to a preliminary evaluation of the proposal. In the third part of the thesis, a proxy is coupled to an error model to provide an approximate response for the two-stage MCMC set-up. We demonstrate an increase in acceptance rate by a factor three with respect to one-stage MCMC results. An open question remains: hof do we choose the size of the learning set and identify the realizations to optimize the construction of the error model. This requires devising an iterative strategy to construct the error model, such that, as new of simulations are performed, the error model is iteratively improved by incorporating the new information. This is discussed in the fourth part of the thesis, in which we apply this methodology to a problem of saline intrusion in a coastal aquifer.

Peatlands and plantations in Sumatra, Indonesia : Complex realities for resource governance, rural development and climate change mitigation

Peatlands play a crucial role in Indonesia's economic development, and in its stated goal of reducing greenhouse gas emissions. Improved peatland management - including a national moratorium on the granting of any new conversion licenses - forms a cornerstone of Indonesia's climate change mitigation commitment. At the same time, rapid expansion of the plantation sector is driving wide-scale drainage and conversion of peat swamp ecosystems. The province of Riau, in central Sumatra, finds itself at the crossroads of these conflicting agendas. This essay presents a case study of three islands on Riau's east coast affected by industrial timber plantation concessions. It examines the divergent experiences, perceptions and responses of communities on the islands. A mix of dramatic protests, localised everyday actions and constructive dialogue has succeeded in delaying or perhaps halting one of the concessions, while negotiations and contestation with the other two continue. With the support of regional and national non-governmental organisations and local government, communities are pursuing alternative development strategies, including the cultivation of sago, which requires no peat drainage. While a powerful political economy of state and corporate actors shapes the contours of socio-environmental change, local social movements can alter trajectories of change, promoting incremental improvements and alternative pathways.

Hope as a Resource for Career Exploration : Examining Incremental and Cross-Lagged Effects

Hope is believed to be beneficial for vocational pursuits, but the question of how and why hope is related to pivotal career development variables remains largely unaddressed. In a series of three studies,we investigated the relationship between hope and career exploration. Study 1 examined at-risk adolescents (N = 228) in Switzerland and showed that hope explains variance in career exploration beyond the significant effects of generalized self-efficacy beliefs and perceived social support. Study 2 found the same result among a group (N=223) of first-year students at a Swiss university with a measure of state hope. Study 3 applied a one-year cross-lagged design with a diverse group of students (N = 266) at a German university to investigate the mutual effects of dispositional hope and career exploration over time. Although both variables were found to be related within and over time, we could not confirm lagged effects in either direction. The results suggest that hope is significantly correlated with career exploration because both are related to personality and social-contextual variables.

Design and evaluation of learning algorithms for dynamic resource management in virtual networks

Network virtualisation is considerably gaining attentionas a solution to ossification of the Internet. However, thesuccess of network virtualisation will depend in part on how efficientlythe virtual networks utilise substrate network resources.In this paper, we propose a machine learning-based approachto virtual network resource management. We propose to modelthe substrate network as a decentralised system and introducea learning algorithm in each substrate node and substrate link,providing self-organization capabilities. We propose a multiagentlearning algorithm that carries out the substrate network resourcemanagement in a coordinated and decentralised way. The taskof these agents is to use evaluative feedback to learn an optimalpolicy so as to dynamically allocate network resources to virtualnodes and links. The agents ensure that while the virtual networkshave the resources they need at any given time, only the requiredresources are reserved for this purpose. Simulations show thatour dynamic approach significantly improves the virtual networkacceptance ratio and the maximum number of accepted virtualnetwork requests at any time while ensuring that virtual networkquality of service requirements such as packet drop rate andvirtual link delay are not affected.

The temporal dynamics of resource use by frugivorous birds: a network approach

Ecological network patterns are influenced by diverse processes that operate at different temporal rates. Here we analyzed whether the coupled effect of local abundance variation, seasonally phenotypic plastic responses, and species evolutionary adaptations might act in concert to shape network patterns. We studied the temporal variation in three interaction properties of bird species (number of interactions per species, interaction strength, and interaction asymmetry) in a temporal sequence of 28 plant frugivore interaction networks spanning two years in a Mediterranean shrubland community. Three main hypotheses dealing with the temporal variation of network properties were tested, examining the effects of abundance, switching behavior between alternative food resources, and morphological traits in determining consumer interaction patterns. Our results demonstrate that temporal variation in consumer interaction patterns is explained by short-term variation in resource and bird abundances and seasonal dietary switches between alternative resources (fleshy fruits and insects). Moreover, differences in beak morphology are associated with differences in switching behavior between resources, suggesting an important role of foraging adaptations in determining network patterns. We argue that beak shape adaptations might determine generalist and specialist feeding behaviors and thus the positions of consumer species within the network. Finally, we provide a preliminary framework to interpret phylogenetic signal in plant animal networks. Indeed, we show that the strength of the phylogenetic signal in networks depends on the relative importance of abundance, behavioral, and morphological variables. We show that these variables strongly differ in their phylogenetic signal. Consequently, we suggest that moderate and significant phylogenetic effects should be commonly observed in networks of species interactions. Read More: http://www.esajournals.org/doi/abs/10.1890/07-1939.1

Consequences of warming and resource quality on the stoichiometry and nutrient cycling of a stream shredder

As a result of climate change, streams are warming and their runoff has been decreasing in most temperate areas. These changes can affect consumers directly by increasing their metabolic rates and modifying their physiology and indirectly by changing the quality of the resources on which organisms depend. In this study, a common stream detritivore (Echinogammarus berilloni Catta) was reared at two temperatures (15 and 20°C) and fed Populus nigra L. leaves that had been conditioned either in an intermittent or permanent reach to evaluate the effects of resource quality and increased temperatures on detritivore performance, stoichiometry and nutrient cycling. The lower quality (i.e., lower protein, soluble carbohydrates and higher C:P and N:P ratios) of leaves conditioned in pools resulted in compensatory feeding and lower nutrient retention capacity by E. berilloni. This effect was especially marked for phosphorus, which was unexpected based on predictions of ecological stoichiometry. When individuals were fed pool-conditioned leaves at warmer temperatures, their growth rates were higher, but consumers exhibited less efficient assimilation and higher mortality. Furthermore, the shifts to lower C:P ratios and higher lipid concentrations in shredder body tissues suggest that structural molecules such as phospholipids are preserved over other energetic C-rich macromolecules such as carbohydrates. These effects on consumer physiology and metabolism were further translated into feces and excreta nutrient ratios. Overall, our results show that the effects of reduced leaf quality on detritivore nutrient retention were more severe at higher temperatures because the shredders were not able to offset their increased metabolism with increased consumption or more efficient digestion when fed pool-conditioned leaves. Consequently, the synergistic effects of impaired food quality and increased temperatures might not only affect the physiology and survival of detritivores but also extend to other trophic compartments through detritivore-mediated nutrient cycling.