Unitary Precoding and Basis Dependency of MMSE Performance for Gaussian Erasure Channels
Journal article, 2014

We consider the transmission of a Gaussian vector source over a multidimensional Gaussian channel where a random or a fixed subset of the channel outputs are erased. Within the setup where the only encoding operation allowed is a linear unitary transformation on the source, we investigate the minimum mean-square error (MMSE) performance, both in average, and also in terms of guarantees that hold with high probability as a function of the system parameters. Under the performance criterion of average MMSE, necessary conditions that should be satisfied by the optimal unitary encoders are established and explicit solutions for a class of settings are presented. For random sampling of signals that have a low number of degrees of freedom, we present MMSE bounds that hold with high probability. Our results illustrate how the spread of the eigenvalue distribution and the unitary transformation contribute to these performance guarantees. The performance of the discrete Fourier transform (DFT) is also investigated. As a benchmark, we investigate the equidistant sampling of circularly wide-sense stationary signals, and present the explicit error expression that quantifies the effects of the sampling rate and the eigenvalue distribution of the covariance matrix of the signal. These findings may be useful in understanding the geometric dependence of signal uncertainty in a stochastic process. In particular, unlike information theoretic measures such as entropy, we highlight the basis dependence of uncertainty in a signal with another perspective. The unitary encoding space restriction exhibits the most and least favorable signal bases for estimation.

discrete fourier transform

compressive sensing

Random field estimation


Ayca Ozcelikkale

Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

S. Yuksel

Queen's University, Kingston

H. M. Ozaktas

Bilkent Universitesi

IEEE Transactions on Information Theory

0018-9448 (ISSN)

Vol. 60 7186-7203 6901254

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering