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Algorithmes itératifs à faible complexité pour le codage de canal et le compressed sensing

Abstract : Iterative algorithms are now widely used in all areas of signal processing and digital communications. In modern communication systems, iterative algorithms are used for decoding low-density parity-check (LDPC) codes, a popular class of error-correction codes that are now widely used for their exceptional error-rate performance. In a more recent field known as compressed sensing, iterative algorithms are used as a method of reconstruction to recover a sparse signal from a linear set of measurements. This thesis primarily deals with the development of low-complexity iterative algorithms for the two aforementioned fields, namely, the design of low-complexity decoding algorithms for LDPC codes, and the development and analysis of a low complexity reconstruction algorithm called Interval-Passing Algorithm (IPA) for compressed sensing. In the first part of this thesis, we address the area of decoding algorithms for LDPC codes. It is well-known that LDPC codes suffer from the error floor phenomenon in spite of their exceptional performance, where traditional iterative decoders based on the belief propagation (BP) fail for certain low-noise configurations. Recently, a novel class of decoders called ''finite alphabet iterative decoders (FAIDs)'' were proposed that are capable of surpassing BP in the error floor at much lower complexity. In this work, we focus on the problem of selection of particularly good FAIDs for column-weight-three codes over the Binary Symmetric channel (BSC). Traditional methods for decoder selection use asymptotic techniques such as the density evolution method, which do not guarantee a good performance on finite-length codes especially in the error floor region. Instead, we propose a methodology for selection that relies on the knowledge of potentially harmful topologies that could be present in a code, using the concept of noisy trapping set. Numerical results are provided to show that FAIDs selected based on our methodology outperform BP in the error floor on several codes. In the second part of this thesis, we address the area of iterative reconstruction algorithms for compressed sensing. Iterative algorithms have been proposed for compressed sensing in order to tackle the complexity of the LP reconstruction method. In this work, we modify and analyze a low complexity reconstruction algorithm called the IPA which uses sparse matrices as measurement matrices. Similar to what has been done for decoding algorithms in the area of coding theory, we analyze the failures of the IPA and link them to the stopping sets of the binary representation of the sparse measurement matrices used. The performance of the IPA makes it a good trade-off between the complex L1-minimization reconstruction and the very simple verification decoding.
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  • HAL Id : tel-00819414, version 1

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Ludovic Danjean. Algorithmes itératifs à faible complexité pour le codage de canal et le compressed sensing. Théorie de l'information [cs.IT]. Université de Cergy Pontoise, 2012. Français. ⟨tel-00819414⟩

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