Browsing FAS Scholarly Articles by Keyword "compressed sensing"

Now showing items 1-7 of 7

    • Adaptive Greedy Algorithm With Application to Nonlinear Communications 

      Mileounis, Garasimos; Babadi, Behtash; Kalouptsidis, Nicholas; Tarokh, Vahid (Institute of Electrical and Electronics Engineers, 2010)
      Greedy algorithms form an essential tool for compressed sensing. However, their inherent batch mode discourages their use in time-varying environments due to significant complexity and storage requirements. In this paper ...

    • Collaborative Compressive Spectrum Sensing in a UAV Environment 

      Chen, Kevin; Kung, H. T.; Vlah, Dario; Hague, Daniel; Muccio, Michael; Poland, Brendon (Institute of Electrical and Electronics Engineers, 2011)
      Spectrum sensing is of fundamental importance to many wireless applications including cognitive radio channel assignment and radiolocation. However, conventional spectrum sensing can be prohibitively expensive in computation ...

    • Measurement Combining and Progressive Reconstruction in Compressive Sensing 

      Chen, Kevin; Kung, H. T.; Vlah, Dario; Suter, Bruce (Institute of Electrical and Electronics Engineers, 2011)
      Compressive sensing has emerged as an important new technique in signal acquisition due to the surprising property that a sparse signal can be captured from measurements obtained at a sub-Nyquist rate. The decoding cost ...

    • Nonlinear Compressive Sensing for Distorted Measurements and Application to Improving Efficiency of Power Amplifiers 

      Chen, Hsieh-Chung; Kung, H.; Comiter, Marcus (IEEE, 2017-05)
      Compressive sensing, which enables signal recovery from fewer samples than traditional sampling theory dictates, assumes that the sampling process is linear. However, this linearity assumption may not hold in the analog ...

    • Partitioned Compressive Sensing with Neighbor-Weighted Decoding 

      Kung, H. T.; Tarsa, Stephen John (Institute of Electrical and Electronics Engineers, 2011)
      Compressive sensing has gained momentum in recent years as an exciting new theory in signal processing with several useful applications. It states that signals known to have a sparse representation may be encoded and later ...

    • Separation-Based Joint Decoding in Compressive Sensing 

      Chen, Kevin; Kung, H. T. (Institute of Electrical and Electronics Engineers, 2011)
      We introduce a joint decoding method for compressive sensing that can simultaneously exploit sparsity of individual components of a composite signal. Our method can significantly reduce the total number of variables decoded ...

    • SPARLS: The Sparse RLS Algorithm 

      Babadi, Behtash; Kalouptsidis, Nicholas; Tarokh, Vahid (Institute of Electrical and Electronics Engineers, 2010)
      We develop a recursive ${cal L}_{1}$-regularized least squares (SPARLS) algorithm for the estimation of a sparse tap-weight vector in the adaptive filtering setting. The SPARLS algorithm exploits noisy observations of the ...