865 research outputs found

    Models and optimisation methods for interference coordination in self-organising cellular networks

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    A thesis submitted for the degree of Doctor of PhilosophyWe are at that moment of network evolution when we have realised that our telecommunication systems should mimic features of human kind, e.g., the ability to understand the medium and take advantage of its changes. Looking towards the future, the mobile industry envisions the use of fully automatised cells able to self-organise all their parameters and procedures. A fully self-organised network is the one that is able to avoid human involvement and react to the fluctuations of network, traffic and channel through the automatic/autonomous nature of its functioning. Nowadays, the mobile community is far from this fully self-organised kind of network, but they are taken the first steps to achieve this target in the near future. This thesis hopes to contribute to the automatisation of cellular networks, providing models and tools to understand the behaviour of these networks, and algorithms and optimisation approaches to enhance their performance. This work focuses on the next generation of cellular networks, in more detail, in the DownLink (DL) of Orthogonal Frequency Division Multiple Access (OFDMA) based networks. Within this type of cellular system, attention is paid to interference mitigation in self-organising macrocell scenarios and femtocell deployments. Moreover, this thesis investigates the interference issues that arise when these two cell types are jointly deployed, complementing each other in what is currently known as a two-tier network. This thesis also provides new practical approaches to the inter-cell interference problem in both macro cell and femtocell OFDMA systems as well as in two-tier networks by means of the design of a novel framework and the use of mathematical optimisation. Special attention is paid to the formulation of optimisation problems and the development of well-performing solving methods (accurate and fast)

    A quasi-static cluster-computing approach for dynamic channelassignment in cellular mobile communication systems

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    Efficient management of the radio spectrum can be accomplished by making use of channel assignment techniques, which work by allocating different channels of the spectrum to the cells of the network in a conflict-free manner (i.e., the co-channel interference is minimized). The problem of dynamically reallocating the channels in response to change in user location patterns, which occurs frequently for a microcell network architecture, is even more difficult to tackle in a timely manner. Most existing approaches use various sequential search-based heuristics which cannot produce high-quality allocation fast enough to cope with the frequent traffic requirement variations. In this paper, we propose a quasi-static approach which combines the merits of both static and dynamic schemes. The static component of our approach uses a parallel genetic algorithm to generate a suite of representative assignments based on a set of different estimated traffic scenarios. At on-line time, the dynamic component observes the actual traffic requirement and retrieves the representative assignment of the closest scenario from the off-line table. The retrieved assignment is then quickly refined by using a fast parallel local search algorithm. Our extensive simulation experiments have indicated that the proposed quasi-static system outperforms other dynamic channel assignment techniques significantly in terms of both blocking probabilities and computational overhead.published_or_final_versio
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