Paper Info
Title
On The Mmse Estimation Of Norm Of A Gaussian Vector Under Additive White Gaussian Noise With Randomly Missing Input Entries
Abstract
This paper considers the task of estimating the l(2) norm of a n-dimensional random Gaussian vector from noisy measurements taken after many of the entries of the vector are missed and only K (0 <= K <= n ) entries are retained while the rest of the entries are erased and set to 0. Specifically, we evaluate the minimum mean square error (MMSE) estimator of the l(2) norm of the unknown Gaussian vector performing measurements under additive white Gaussian noise (AWGN) on the vector after the data missing and derive expressions for the corresponding mean square error (MSE). We find that the corresponding MSE normalized by n tends to 0 as n -> infinity for any 1 <= k <= n . Furthermore, expressions for the MSE is derived when the variance of the AWGN noise tends to either 0 or infinity. These results generalize the results of Dytso et al. [1] where the case K = n is considered, i.e. the MMSE estimator of norm of random Gaussian vector is derived from measurements under AWGN noise without considering the data missing phenomenon. (C) 2020 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.sigpro.2020.107848
SIGNAL PROCESSING
Keywords
DocType
Volume
MMSE estimation, Additive white Gaussian noise (AWGN), Missing data
Journal
179
ISSN
Citations 
PageRank 
0165-1684
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Samrat Mukhopadhyay111.79