Unveiling bias compensation in turbo-based algorithms for (discrete) compressed sensing. Sparrer, S. & Fischer, R. F. H. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 2091-2095, Aug, 2017.
Unveiling bias compensation in turbo-based algorithms for (discrete) compressed sensing [pdf]Paper  doi  abstract   bibtex   
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.

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