By Topic

SNR and Noise Variance Estimation for MIMO Systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Das, A. ; ViaSat, Inc., Carlsbad, CA, USA ; Rao, B.D.

Accurate signal-to-noise ratio (SNR) and noise variance estimation are extremely important aspects of receiver design in multiple-input multiple-output (MIMO) systems. Typically, these parameters are estimated using known pilot/training symbols. However, significant improvements may be made by using both the known pilot symbols as well as the unknown data symbols. In this paper, we address SNR and noise variance estimation of MIMO systems for both a data aided (DA) model, a non-data aided (NDA) model, as well as a mixed model that uses known and unknown data symbols. The Cramér-Rao lower bound (CRLB) and modified Cramér-Rao lower bound (MCRLB) for MIMO SNR and MIMO noise variance estimation are determined for digital constellations such as BPSK, QPSK, 8PSK, and 16QAM. Maximum-likelihood estimators are derived in closed form for the DA model. For the NDA model, closed form approximations are derived in addition to iterative expectation-maximization (EM) algorithm based estimators, all of which are demonstrated to perform very close to the CRLB.

Published in:

Signal Processing, IEEE Transactions on  (Volume:60 ,  Issue: 8 )