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Comment on "Asymptotic Achievability of the Cram\'{e}r-Rao Bound for Noisy Compressive Sampling"

Information Theory 2015-09-16 v1 math.IT

Abstract

In [1], we proved the asymptotic achievability of the Cram\'{e}r-Rao bound in the compressive sensing setting in the linear sparsity regime. In the proof, we used an erroneous closed-form expression of ασ2\alpha \sigma^2 for the genie-aided Cram\'{e}r-Rao bound σ2Tr(AIAI)1\sigma^2 \textrm{Tr} (\mathbf{A}^*_\mathcal{I} \mathbf{A}_\mathcal{I})^{-1} from Lemma 3.5, which appears in Eqs. (20) and (29). The proof, however, holds if one avoids replacing σ2Tr(AIAI)1\sigma^2 \textrm{Tr} (\mathbf{A}^*_\mathcal{I} \mathbf{A}_\mathcal{I})^{-1} by the expression of Lemma 3.5, and hence the claim of the Main Theorem stands true. In Chapter 2 of the Ph. D. dissertation by Behtash Babadi [2], this error was fixed and a more detailed proof in the non-asymptotic regime was presented. A draft of Chapter 2 of [2] is included in this note, verbatim. We would like to refer the interested reader to the full dissertation, which is electronically archived in the ProQuest database [2], and a draft of which can be accessed through the author's homepage under: http://ece.umd.edu/~behtash/babadi_thesis_2011.pdf.

Keywords

Cite

@article{arxiv.1509.04375,
  title  = {Comment on "Asymptotic Achievability of the Cram\'{e}r-Rao Bound for Noisy Compressive Sampling"},
  author = {Behtash Babadi and Nicholas Kalouptsidis and Vahid Tarokh},
  journal= {arXiv preprint arXiv:1509.04375},
  year   = {2015}
}