Title: Unveiling Bias Compensation in Turbo-Based Algorithms for (Discrete) Compressed Sensing,
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.