Ber analysis of the box relaxation for BPSK signal recovery

Handle URI:
http://hdl.handle.net/10754/623517
Title:
Ber analysis of the box relaxation for BPSK signal recovery
Authors:
Thrampoulidis, Christos; Abbasi, Ehsan; Xu, Weiyu; Hassibi, Babak
Abstract:
We study the problem of recovering an n-dimensional BPSK signal from m linear noise-corrupted measurements using the box relaxation method which relaxes the discrete set {±1}n to the convex set [-1,1]n to obtain a convex optimization algorithm followed by hard thresholding. When the noise and measurement matrix have iid standard normal entries, we obtain an exact expression for the bit-wise probability of error Pe in the limit of n and m growing and m/n fixed. At high SNR our result shows that the Pe of box relaxation is within 3dB of the matched filter bound (MFB) for square systems, and that it approaches the (MFB) as m grows large compared to n. Our results also indicate that as m, n → ∞, for any fixed set of size k, the error events of the corresponding k bits in the box relaxation method are independent.
Citation:
Thrampoulidis C, Abbasi E, Xu W, Hassibi B (2016) Ber analysis of the box relaxation for BPSK signal recovery. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Available: http://dx.doi.org/10.1109/icassp.2016.7472383.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Conference/Event name:
41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Issue Date:
24-Jun-2016
DOI:
10.1109/icassp.2016.7472383
Type:
Conference Paper
Sponsors:
This work was supported in part by the National Science Foundation under grants CNS-0932428, CCF-1018927, CCF-1423663 and CCF-1409204, by a grant from Qualcomm Inc., by NASAs Jet Propulsion Laboratory through the President and Directors Fund, by King Abdulaziz University, and by King Abdullah University of Science and Technology. Xu’s work is supported by Simons Foundation, Iowa Energy Center, KAUST, and NIH 1R01EB020665-01.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorThrampoulidis, Christosen
dc.contributor.authorAbbasi, Ehsanen
dc.contributor.authorXu, Weiyuen
dc.contributor.authorHassibi, Babaken
dc.date.accessioned2017-05-15T10:35:06Z-
dc.date.available2017-05-15T10:35:06Z-
dc.date.issued2016-06-24en
dc.identifier.citationThrampoulidis C, Abbasi E, Xu W, Hassibi B (2016) Ber analysis of the box relaxation for BPSK signal recovery. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Available: http://dx.doi.org/10.1109/icassp.2016.7472383.en
dc.identifier.doi10.1109/icassp.2016.7472383en
dc.identifier.urihttp://hdl.handle.net/10754/623517-
dc.description.abstractWe study the problem of recovering an n-dimensional BPSK signal from m linear noise-corrupted measurements using the box relaxation method which relaxes the discrete set {±1}n to the convex set [-1,1]n to obtain a convex optimization algorithm followed by hard thresholding. When the noise and measurement matrix have iid standard normal entries, we obtain an exact expression for the bit-wise probability of error Pe in the limit of n and m growing and m/n fixed. At high SNR our result shows that the Pe of box relaxation is within 3dB of the matched filter bound (MFB) for square systems, and that it approaches the (MFB) as m grows large compared to n. Our results also indicate that as m, n → ∞, for any fixed set of size k, the error events of the corresponding k bits in the box relaxation method are independent.en
dc.description.sponsorshipThis work was supported in part by the National Science Foundation under grants CNS-0932428, CCF-1018927, CCF-1423663 and CCF-1409204, by a grant from Qualcomm Inc., by NASAs Jet Propulsion Laboratory through the President and Directors Fund, by King Abdulaziz University, and by King Abdullah University of Science and Technology. Xu’s work is supported by Simons Foundation, Iowa Energy Center, KAUST, and NIH 1R01EB020665-01.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectMaximum Likelihood Decoderen
dc.subjectBER Analysisen
dc.subjectBox Relaxationen
dc.subjectBPSKen
dc.subjectMatched Filter Bounden
dc.titleBer analysis of the box relaxation for BPSK signal recoveryen
dc.typeConference Paperen
dc.identifier.journal2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)en
dc.conference.date2016-03-20 to 2016-03-25en
dc.conference.name41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016en
dc.conference.locationShanghai, CHNen
dc.contributor.institutionDepartment of Electrical Engeeniring, Caltech, Pasadena, USAen
dc.contributor.institutionDepartment of ECE, University of Iowaen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.