@InProceedings{8081578,
  author = {S. Sparrer and R. F. H. Fischer},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Unveiling bias compensation in turbo-based algorithms for (discrete) compressed sensing},
  year = {2017},
  pages = {2091-2095},
  abstract = {In Compressed Sensing, a real-valued sparse vector has to be recovered from an underdetermined system of linear equations. In many applications, however, the elements of the sparse vector are drawn from a finite set. Adapted algorithms incorporating this additional knowledge are required for the discrete-valued setup. In this paper, turbo-based algorithms for both cases are elucidated and analyzed from a communications engineering perspective, leading to a deeper understanding of the algorithm. In particular, we gain the intriguing insight that the calculation of extrinsic values is equal to the unbiasing of a biased estimate, and present an improved algorithm.},
  keywords = {compressed sensing;turbo codes;vectors;real-valued sparse vector;underdetermined system;linear equations;finite set;discrete-valued setup;communications engineering perspective;bias compensation;compressed sensing;turbo-based algorithms;Signal processing algorithms;Estimation;Approximation algorithms;Sparse matrices;Compressed sensing;Matching pursuit algorithms;Decoding},
  doi = {10.23919/EUSIPCO.2017.8081578},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570346908.pdf},
}

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