Unveiling Bias Compensation in Turbo-Based Algorithms for (Discrete) Compressed Sensing
Information Theory
2017-03-03 v1 math.IT
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.
Cite
@article{arxiv.1703.00707,
title = {Unveiling Bias Compensation in Turbo-Based Algorithms for (Discrete) Compressed Sensing},
author = {Susanne Sparrer and Robert F. H. Fischer},
journal= {arXiv preprint arXiv:1703.00707},
year = {2017}
}