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.

Paper doi abstract bibtex

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.

@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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