Enhanced iterative hard thresholding for the estimation of discrete-valued sparse signals

Sparrer, Susanne; Fischer, Robert F. H.

doi:10.1109/eusipco.2016.7760212

Cited by 4 publications

(1 citation statement)

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“…Since this approach takes the apriori distribution of x into account, it depends on the alphabet; an adaptation to any alphabet is straightforward. This approach is also used in other algorithms for (discrete) CS, cf., e.g., [15], [22], [24], [13]. All variables of the second (soft-value calculating) step are indicated by the index "S".…”

Section: A Approximate Lmmse Tsrmentioning

confidence: 99%

Unveiling bias compensation in turbo-based algorithms for (discrete) compressed sensing

Sparrer

Fischer

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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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.

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Section: A Approximate Lmmse Tsrmentioning

confidence: 99%