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dc.contributor.authorBen Atitallah, Ismail
dc.contributor.authorThrampoulidis, Christos
dc.contributor.authorKammoun, Abla
dc.contributor.authorAl-Naffouri, Tareq Y.
dc.contributor.authorHassibi, Babak
dc.contributor.authorAlouini, Mohamed-Slim
dc.date.accessioned2017-10-03T12:49:30Z
dc.date.available2017-10-03T12:49:30Z
dc.date.issued2017-06-20
dc.identifier.citationBen Atitallah I, Thrampoulidis C, Kammoun A, Al-Naffouri TY, Hassibi B, et al. (2017) BER analysis of regularized least squares for BPSK recovery. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Available: http://dx.doi.org/10.1109/ICASSP.2017.7952960.
dc.identifier.doi10.1109/ICASSP.2017.7952960
dc.identifier.urihttp://hdl.handle.net/10754/625622
dc.description.abstractThis paper investigates the problem of recovering an n-dimensional BPSK signal x0 {’1, 1}$^{n}$ from m-dimensional measurement vector y = Ax+z, where A and z are assumed to be Gaussian with iid entries. We consider two variants of decoders based on the regularized least squares followed by hard-thresholding: the case where the convex relaxation is from {’1, 1}$^{n}$ to „ $^{n}$ and the box constrained case where the relaxation is to [’1, 1]$^{n}$. For both cases, we derive an exact expression of the bit error probability when n and m grow simultaneously large at a fixed ratio. For the box constrained case, we show that there exists a critical value of the SNR, above which the optimal regularizer is zero. On the other side, the regularization can further improve the performance of the box relaxation at low to moderate SNR regimes. We also prove that the optimal regularizer in the bit error rate sense for the unboxed case is nothing but the MMSE detector.
dc.description.sponsorshipThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/2221-01.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/7952960/
dc.subjectBinary phase shift keying
dc.subjectBit error rate
dc.subjectClosed-form solutions
dc.subjectDecoding
dc.subjectDetectors
dc.subjectError probability
dc.subjectSignal to noise ratio
dc.titleBER analysis of regularized least squares for BPSK recovery
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journal2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
dc.conference.date2017-03-05 to 2017-03-09
dc.conference.name2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
dc.conference.locationNew Orleans, LA, USA
dc.contributor.institutionDepartment of Electrical Engeeniring, Caltech, Pasadena, USA
kaust.personBen Atitallah, Ismail
kaust.personKammoun, Abla
kaust.personAl-Naffouri, Tareq Y.
kaust.personAlouini, Mohamed-Slim
kaust.grant.numberURF/1/2221-01
dc.date.published-online2017-06-20
dc.date.published-print2017-03


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