Techniques for MIMO Channel Covariance Matrix Quantization

In this correspondence, we develop techniques to efficiently quantize channel covariance matrices in multiple-input multiple-output (MIMO) Rayleigh fading environments. While these covariance matrices change less frequently than the channel matrices themselves, this information needs to be updated w...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on signal processing 2012-06, Vol.60 (6), p.3340-3345
Main Authors: Ghosh, S., Rao, B. D., Zeidler, J. R.
Format: Article
Language:English
Subjects:
Applied sciences
Approximation methods
Channel distribution information
channel quantization
correlated rayleigh fading
Correlation
covariance feedback
Covariance matrix
Detection, estimation, filtering, equalization, prediction
Eigenvalues and eigenfunctions
Exact sciences and technology
Information, signal and communications theory
MIMO
Sampling, quantization
Signal and communications theory
Signal, noise
Telecommunications and information theory
Transmitters
Vectors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this correspondence, we develop techniques to efficiently quantize channel covariance matrices in multiple-input multiple-output (MIMO) Rayleigh fading environments. While these covariance matrices change less frequently than the channel matrices themselves, this information needs to be updated when it does change. Furthermore, these covariance matrices have significantly more parameters to quantize than their channel matrix counterparts. Since many applications focus on utilizing the strongest eigenmodes of the channel covariance matrix and since these matrices tend to be low-rank, we focus on efficiently quantizing the dominant eigenvectors of these matrices. We develop Lloyd-type algorithms based on training data from the environment to develop our codebooks. We also develop an algorithm based on the reduced-parameter Kronecker and Weichselberger models to generate codebooks with reduced real-time codeword search.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2012.2190727