22 research outputs found

    Quantization and Compressive Sensing

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    Quantization is an essential step in digitizing signals, and, therefore, an indispensable component of any modern acquisition system. This book chapter explores the interaction of quantization and compressive sensing and examines practical quantization strategies for compressive acquisition systems. Specifically, we first provide a brief overview of quantization and examine fundamental performance bounds applicable to any quantization approach. Next, we consider several forms of scalar quantizers, namely uniform, non-uniform, and 1-bit. We provide performance bounds and fundamental analysis, as well as practical quantizer designs and reconstruction algorithms that account for quantization. Furthermore, we provide an overview of Sigma-Delta (ΣΔ\Sigma\Delta) quantization in the compressed sensing context, and also discuss implementation issues, recovery algorithms and performance bounds. As we demonstrate, proper accounting for quantization and careful quantizer design has significant impact in the performance of a compressive acquisition system.Comment: 35 pages, 20 figures, to appear in Springer book "Compressed Sensing and Its Applications", 201

    Decision feedback multiuser detection: a systematic approach

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    Diversity order gain for narrow-band multiuser communications with pre-combining group detection

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    An Information-Theoretic Framework for Deriving Canonical Decision-Feedback Receivers in Gaussian Channels

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    Optimum Noncoherent Multiuser Decision Feedback Detection

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    Analysis of Decision Feedback Detection for MIMO Rayleigh-Fading Channels and the Optimization of Power and Rate Allocations

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    Optimally near-far resistant multiuser detection in differentially coherent synchronous channels

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    Blind adaptive multiuser detection for cellular systems using stochastic approximation with averaging

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    Maximal Diversity Algebraic Space–Time Codes With Low Peak-to-Mean Power Ratio

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