کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6958069 | 1451936 | 2017 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Asymptotic MMSE analysis under sparse representation modeling
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Compressed sensing is a signal processing technique in which data is acquired directly in a compressed form. There are two modeling approaches that can be considered: the worst-case (Hamming) approach and a statistical mechanism, in which the signals are modeled as random processes rather than as individual sequences. In this paper, the second approach is studied. In particular, we consider a model of the form Y=HX+W, where each comportment of X is given by Xi=SiUi, where {Ui} are independent and identically distributed (i.i.d.) Gaussian random variables, {Si} are binary i.i.d. random variables independent of {Ui}, HâRkÃn is a random matrix with i.i.d. entries, and W is white Gaussian noise. Using a direct relationship between optimum estimation and certain partition functions, and by invoking methods from statistical mechanics and from random matrix theory (RMT), we derive an asymptotic formula for the minimum mean-square error (MMSE) of estimating the input vector X given Y and H, as k,nââ, keeping the measurement rate, R=k/n, fixed. In contrast to previous derivations, which are based on the replica method, the analysis carried out in this paper is rigorous.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 131, February 2017, Pages 320-332
Journal: Signal Processing - Volume 131, February 2017, Pages 320-332
نویسندگان
Wasim Huleihel, Neri Merhav,