Compressive Sensing in Wireless Communications

Contact: Carsten Bockelmann

Involved Staff

Description

Compressive Sensing is a novel field in digital signal processing that is concerned with the efficient sampling and reconstruction of compressible signals. In the field of CS compressibility is often defined by a sparse respresentation of a given signal in an appropriate basis (Fourier, Wavelets, etc.). Thus, reconstruction of signals is focused on under-determined equations with sparse signals. This very general idea opens a broad range of applications of which two are focused here:

Fields of Application:

  • Application of Compressive Sensing for data detection in wireless digital communications
  • Efficient signal aquisition and processing in invasive neural implants

 Work tasks (at present):

  • Design and study of CS detection concepts such as:
    • L1 / L2 Optimization
    • Greedy algorithms such as Orthogonal Matching Pursuit (OMP), Orthogonal Least Squares (OLS), CoSaMP
    • Belief Propagation (AMP, GAMP)
  • Application of CS to communication systems / sensor communication
    • Impact of channel coding
    • Communication specific sparsity structure
    • Activity detection (false alarms, missed detections)
    • Cross-Layer PHY/MAC design issues
  • CS in neuro sciences
    • Appropriate bases for neural signals (action potentials, local field potential)
    • Correlation modelling and exploitation in CS
    • Hardware efficient CS approaches
  • Theoretical studies on performance limits and complexity

    Projects

    Publications

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    2019

    2018

    2017

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    Last change on 16.03.2017 by Admin
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