<p>The traditional way of increasing capacity in a wirelesscommunication system has been cell splitting and fixedchannel-allocation based on prediction tools. However, theplanning complexity increases rapidly with the number of cellsand the method is not suitable for the large temporal andspatial traffic variations expected in the future. A lot ofresearch has therefore been performed regarding adaptivechannel allocation, where a channel can be used anywhere aslong as the signal-to-interference ratio (SIR) is acceptable. Acommon opinion is that these solutions must be decentralizedsince a centralized one would be overly complex.</p><p>In this thesis, we study the locally centralized<i>bunch concept</i>for radio resource management (RRM) in aManhattan environment and show that it can give a very highcapacity both for outdoor users and for indoor users covered byoutdoor base stations. We show how measurement limitations anderrors affect the performance and wepropose methods to handlethese problems, e.g. averaging of measured values, robustchannel selection algorithms, and increased SIR margins. Wealso study the computational and signaling complexities andshow that they can be reduced by splitting large bunches, usingsparse matrix calculations, and by using a simplified admissionalgorithm. However, a reduction of the complexity often means areduction of the system capacity.</p><p>The measurements needed for RRM can also be used to find amobile terminal's geographical position. We propose and studysome simple yet accurate methods for this purpose. We alsostudy if position information can enhance RRM as is oftensuggested in the literature. In the studied scenario, thisinformation seems to be of limited use. One possible use is toestimate the mobile user's speed, to assist handover decisions.Another use is to find the location of user hotspots in anarea, which is beneficial for system planning.</p><p>Our results show that the bunch concept is a promisingcandidate for radio resource management in future wirelesssystems. We believe that the complexity is manageable and themain price we have to pay for high capacity is frequentreallocation of connections.</p><p><b>Keywords:</b>bunch concept, radio resource management,network-assisted resource management, base station selection,dynamic channel allocation, DCA, channel selection,least-interfered, interference avoidance, interferenceaveraging, handover, power control, path-loss measurements,signal strength, link-gain matrix, TD-CDMA, UTRA TDD, Manhattanscenario, microcells, mobile positioning, position accuracy,trilateration, triangulation, speed estimation</p>

[pt] Este trabalho investiga o comportamento de métodos de alocação de canais para Sistemas Móveis Celulares quando associados ao uso de canais de guarda para priorizar chamadas que estejam em handoff. O objetivo desta associação é uma maior qualidade da ligação comparada à obtida quando não se faz uso dessa técnica. Resultados de simulações mostram que este objetivo pode ser atingido. / [en] This work investigates the behavior of channel allocation schemes for Mobile Cellular Systems, when associated with channel guards giving priority to calls that are in handoff procedure. The objective of this association is a better call quality compared to the one in which this technique is not used. Simulation results show that indeed this can be achieved.

Объектом исследования являются медь и малолегированные бронзы, подвергнутые интенсивной пластической деформации. Целью работы было определение текущего состояния научных исследований по использованию меди и малолегированных бронз. А также предложить состав и исследовать структуру и свойства бронзы выбранного состава после интенсивной пластической деформации методом динамического канально-углового прессования. В процессе работы проводился анализ литературных источников. В результате исследования было установлено текущее состояние по проблематике работы и определен вектор исследований. Методами оптической металлографии, дюрометрического анализа, просвечивающей электронной микроскопии и сканирующей электронной микроскопии с применением дисперсионно-энергетического анализа и анализа на основе обратного рассеяния электронов было проведено исследование сплава Cu-0,8 вес.% Hf. При различных обработках методом динамического канально-углового прессования. Эксперимент включал различное состояние бронзы перед деформированием. Также было проведено исследование термической стабильности полученных методом динамического канально-углового прессования образцов. Было установлено, что гафниевая бронза после прессования обладает высокими механическими характеристиками и ультрамелкодисперсной структурой. При этом полученная структура стабильна при комнатной температуре и при последующем отжиге вплоть до 400 °С. Что позволяет рекомендовать данный материал как основу для широкого спектра электротехнических изделий. / The object of research is copper and low-alloy bronzes after severe plastic deformation. The purpose of this work was to review scientific research on the use of copper and low-alloy bronzes. As well as the development of the composition and study of the structure and properties of bronze after severe plastic deformation by the method of dynamic channel-angular pressing had been carried out. Literature analysis was carried out. Methods of optical metallography, durometric analysis, transmission electron microscopy and scanning electron microscopy using energy dispersion analysis and analysis based on electron backscattering had been used to study the Cu-0.8 wt.% Hf alloy. Various processings had been carried out by the method of dynamic canal-angular pressing. The state of the bronze before deformation was variouse. We also studied the thermal stability of samples obtained by the method of dynamic channel-angular pressing. It was found that hafnium bronze after pressing has high mechanical characteristics and an ultrafine structure. In this case, the resulting structure is stable at room temperature and upon subsequent annealing up to 400 °C. That allows us to recommend this material as a basis for a wide range of electrical products.

L'objectif de cette thèse est d'étudier le problème de la détection de données dans le système de communication sans fil, à la fois pour le cas de l'information d'état de canal parfaite et imparfaite au niveau du récepteur. Comme on le sait, la complexité de MLSE est exponentielle en la mémoire de canal et la cardinalité de l'alphabet symbole est rapidement ingérable, ce qui force à recourir à des approches sousoptimales. Par conséquent, en premier lieu, nous proposons une nouvelle égalisation itérative lorsque le canal est inconnu à l'émetteur et parfaitement connu au niveau du récepteur. Ce récepteur est basé sur une approche de continuation, et exploite l'idée d'approcher une fonction originale de coût d'optimisation par une suite de fonctions plus dociles et donc de réduire la complexité de calcul au récepteur.En second lieu, en vue de la détection de données sous un canal dynamique linéaire, lorsque le canal est inconnu au niveau du récepteur, le récepteur doit être en mesure d'effectuer conjointement l'égalisation et l'estimation de canal. De cette manière, on formule une représentation de modèle état-espace combiné du système de communication. Par cette représentation, nous pouvons utiliser le filltre de Kalman comme le meilleur estimateur des paramètres du canal. Le but de cette section est de motiver de façon rigoureuse la mise en place du filltre de Kalman dans l'estimation des sequences de Markov par des canaux dynamiques Gaussien. Par la présente, nous interprétons et explicitons les approximations sous-jacentes dans les approaches heuristiques.Enfin, si nous considérons une approche plus générale pour le canal dynamique non linéaire, nous ne pouvons pas utiliser le filtre de Kalman comme le meilleur estimateur. Ici, nous utilisons des modèles commutation d’espace-état (SSSM) comme modèles espace-état non linéaires. Ce modèle combine le modèle de Markov caché (HMM) et le modèle espace-état linéaire (LSSM). Pour l'estimation de canal et la detection de données, l'approche espérance et maximisation (EM) est utilisée comme approche naturelle. De cette façon, le filtre de Kalman étendu (EKF) et les filtres à particules sont évités. / The aim of this thesis is to study the problem of data detection in wireless communication system, for both case of perfect and imperfect channel state information at the receiver. As well known, the complexity of MLSE being exponential in the channel memory and in the symbol alphabet cardinality is quickly unmanageable and forces to resort to sub-optimal approaches. Therefore, first we propose a new iterative equalizer when the channel is unknown at the transmitter and perfectly known at the receiver. This receiver is based on continuation approach, and exploits the idea of approaching an original optimization cost function by a sequence of more tractable functions and thus reduce the receiver's computational complexity. Second, in order to data detection under linear dynamic channel, when the channel is unknown at the receiver, the receiver must be able to perform joint equalization and channel estimation. In this way, we formulate a combined state-space model representation of the communication system. By this representation, we can use the Kalman filter as the best estimator for the channel parameters. The aim in this section is to motivate rigorously the introduction of the Kalman filter in the estimation of Markov sequences through Gaussian dynamical channels. By this we interpret and make clearer the underlying approximations in the heuristic approaches. Finally, if we consider more general approach for non linear dynamic channel, we can not use the Kalman filter as the best estimator. Here, we use switching state-space model (SSSM) as non linear state-space model. This model combines the hidden Markov model (HMM) and linear state-space model (LSSM). In order to channel estimation and data detection, the expectation and maximization (EM) procedure is used as the natural approach. In this way extended Kalman filter (EKF) and particle filters are avoided.

Nos últimos anos, as redes de comunicação móveis se tornaram de fundamental importância para a infraestrutura dos sistemas de comunicação. Uma das áreas de maior crescimento é a computação móvel. Realizada através de sinais de rádio, a quantidade de canais disponíveis raramente é suficiente para atender a crescente demanda. Este trabalho apresenta uma solução para a questão da alocação de canais, um tópico desafiador dentro da área de redes móveis. A implementação de alocação dinâmica com uso de técnicas computacionais clássicas melhora a utilização dos recursos disponíveis,mas necessita de ajustes periódicos para se adequar a novos cenários. Para a construção de um sistema mais flexível e adaptável, a abordagem escolhida utiliza técnicas de Inteligência Artificial. O modelo proposto combina Teoria Nebulosa, Redes Neurais Artificiais e Sistemas Multi-Agentes. As características de cada técnica foram analisadas e identificamos as partes do sistema que poderiam ser beneficiadas por cada uma. O sistema é resultado da combinação coordenada das três técnicas, e constitui um método eficiente e flexível para gerenciamento de recursos de rádio. Após o detalhamento do modelo, realizamos uma simulação de uma rede celular com o sistema proposto e seu comportamento é comparado com uma rede de referência, para verificação das diferenças e melhorias alcançadas. Por fim, apresentamos a situação atual da pesquisa e os possíveis caminhos para aprimoramento do sistema. / In the last years, mobile networks became more important for communication systems’ infrastructure. One area of great growth is mobile computation, which is performed through radio signals. The amount of available channels rarely is enough to attend the increasing demand. This work presents a solution for the channel allocation topic, a challenging topic inside mobile networks area. The implementation of dynamic allocation using classic computational techniques improves the use of available resources, but it needs periodic and frequent adjustments for new scenarios. The construction of a more flexible and adaptable system was achieved using Artificial Intelligence techniques. Proposed model combines Fuzzy Logic, Artificial Neural Networks and Multi-Agents Systems. Features of each technique had been analyzed and we identified the system modules which could be benefited by them. The system is the result of coordinated combination of these three techniques, and constitutes an efficient and flexible method for radio resources management. After model detailing, we executed a cellular network simulation using proposed system, and its behavior is compared with a reference network, presenting reached differences and improvements. Finally, we present current situation of this research and possible ways for system improvement.

Nos últimos anos, as redes de comunicação móveis se tornaram de fundamental importância para a infraestrutura dos sistemas de comunicação. Uma das áreas de maior crescimento é a computação móvel. Realizada através de sinais de rádio, a quantidade de canais disponíveis raramente é suficiente para atender a crescente demanda. Este trabalho apresenta uma solução para a questão da alocação de canais, um tópico desafiador dentro da área de redes móveis. A implementação de alocação dinâmica com uso de técnicas computacionais clássicas melhora a utilização dos recursos disponíveis,mas necessita de ajustes periódicos para se adequar a novos cenários. Para a construção de um sistema mais flexível e adaptável, a abordagem escolhida utiliza técnicas de Inteligência Artificial. O modelo proposto combina Teoria Nebulosa, Redes Neurais Artificiais e Sistemas Multi-Agentes. As características de cada técnica foram analisadas e identificamos as partes do sistema que poderiam ser beneficiadas por cada uma. O sistema é resultado da combinação coordenada das três técnicas, e constitui um método eficiente e flexível para gerenciamento de recursos de rádio. Após o detalhamento do modelo, realizamos uma simulação de uma rede celular com o sistema proposto e seu comportamento é comparado com uma rede de referência, para verificação das diferenças e melhorias alcançadas. Por fim, apresentamos a situação atual da pesquisa e os possíveis caminhos para aprimoramento do sistema. / In the last years, mobile networks became more important for communication systems’ infrastructure. One area of great growth is mobile computation, which is performed through radio signals. The amount of available channels rarely is enough to attend the increasing demand. This work presents a solution for the channel allocation topic, a challenging topic inside mobile networks area. The implementation of dynamic allocation using classic computational techniques improves the use of available resources, but it needs periodic and frequent adjustments for new scenarios. The construction of a more flexible and adaptable system was achieved using Artificial Intelligence techniques. Proposed model combines Fuzzy Logic, Artificial Neural Networks and Multi-Agents Systems. Features of each technique had been analyzed and we identified the system modules which could be benefited by them. The system is the result of coordinated combination of these three techniques, and constitutes an efficient and flexible method for radio resources management. After model detailing, we executed a cellular network simulation using proposed system, and its behavior is compared with a reference network, presenting reached differences and improvements. Finally, we present current situation of this research and possible ways for system improvement.

[pt] Os esquemas de alocação fixa de canais - FCA - conferem aos Sistemas de Comunicação Móveis Celulares estabilidade com o custo da necessidade de um pré-planejamento de freqüências, trazendo como conseqüência a baixa tolerância à variabilidade de tráfego. Algoritmos de alocação dinâmica de canais - DCA - tem sido propostos por diversos autores com o intuito de minimizar estes problemas, permitindo ao sistema flexibilidade no momento da escolha do canal candidato para servir a uma ligação. Esquemas de alocação híbrida de canais - HCA, combinam as técnicas de alocação fixa com alocação dinâmica de canais, conferindo ao mesmo tempo estabilidade e flexibilidade ao sistema.Este trabalho compara os algoritmos FCA, DCA e HCA sobre um mesmo cenário de simulação, permitindo analisar o desempenho dos mesmos. O esquema FCA utilizado é o mais simples, onde o primeiro canal com o nível aceitável de relação sinal interferência é escolhido para ser alocado. O esquema DCA utiliza a técnica de Segregação de Canais - CS, permitindo ao sistema maior flexibilidade no momento da escolha do canal candidato, devido a não existência do pré-planejamento de freqüências. O terceiro algoritmo, HCA, combina os dois esquemas anteriores. Ao final, será analisado o impacto de se priorizar o procedimento de handoff utilizando-se a técnica conhecida como Canais de Guarda. / [en] Fixed channel assignment -FCA- brings to the Cellular Communication Systems stability at the cost of the use of frequency planning, leading to low tolerance to traffic variability. Dynamic channel assignment -DCA- algorithms have been proposed by several authors in order to minimize these problems,incorporating flexibility to the system with respect to channel selection. Hybrid channel assignment - HCA- combines the techniques of fixed and dynamic channel assignment, giving to the system stability and flexibility at the same time. This work compares the FCA, DCA and HCA algorithms on the same simulation scenario, allowing a complete analysis of these approaches. The FCA used is the simplest. In this algorithm, the first channel with an acceptable level of signal to interference ratio is chosen to be allocated. The DCA uses the technique of Channel Segregation -CS-, a distributed self-learning algorithm that is shown to yield very good performance. The third algorithm,HCA, combine the two previous techniques. At the end, the impact of prioritizing the procedure of handoff will be analyzed, using the technique known as Guard Channel.