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Optimization strategies for blind multiuser detection in strong interference scenarios

Abstract : In this thesis we address the blind channel estimation and source detection of linear mixtures with a single sensor in scenarios strongly dominated by interference. In this framework our only assumptions relate to the sources mutual independence, as well as the discrete and uniform nature of their probability distribution. Based on existing iterative algorithms from the literature, we propose several initialization strategies so as to enhance both their overall performance and robustness to highly unfavorable mixture configurations. We provide a detailed analysis of the relation between the spurious fixed points these algorithms are known to possibly converge to and the underlying mixtures geometry. Possible strategies to account for this additional information in the overall detection process are discussed as well. Simulation results attest to a significant improvement of the achieved error rates compared to all tested traditional detection schemes. An extension of the method to the estimation of frequency-selective channels in multiuser and orthogonal multicarrier transmissions is then performed, along with several initialization propositions. We conclude our study by more general considerations on clustering algorithms and their ability to discriminate between partially entangled data classes.
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Stanley Smith. Optimization strategies for blind multiuser detection in strong interference scenarios. Signal and Image Processing. Conservatoire national des arts et metiers - CNAM, 2019. English. ⟨NNT : 2019CNAM1273⟩. ⟨tel-02515982⟩

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