DocumentCode :
27724
Title :
Pilot Beam Pattern Design for Channel Estimation in Massive MIMO Systems
Author :
Song Noh ; Zoltowski, M.D. ; Youngchul Sung ; Love, David J.
Author_Institution :
Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA
Volume :
8
Issue :
5
fYear :
2014
fDate :
Oct. 2014
Firstpage :
787
Lastpage :
801
Abstract :
In this paper, the problem of pilot beam pattern design for channel estimation in massive multiple-input multiple-output systems with a large number of transmit antennas at the base station is considered, and a new algorithm for pilot beam pattern design for optimal channel estimation is proposed under the assumption that the channel is a stationary Gauss-Markov random process. The proposed algorithm designs the pilot beam pattern sequentially by exploiting the properties of Kalman filtering and the associated prediction error covariance matrices and also the channel statistics such as spatial and temporal channel correlation. The resulting design generates a sequentially-optimal sequence of pilot beam patterns with low complexity for a given set of system parameters. Numerical results show the effectiveness of the proposed algorithm.
Keywords :
Gaussian processes; MIMO communication; Markov processes; antenna radiation patterns; channel estimation; covariance matrices; random processes; transmitting antennas; Kalman filtering; base station; channel statistics; massive MIMO system; multiple input multiple output system; optimal channel estimation; pilot beam pattern design; prediction error covariance matrices; stationary Gauss-Markov random process; system parameters; transmit antenna; Channel estimation; Correlation; Covariance matrices; Kalman filters; MIMO; Training; Vectors; Channel estimation; massive MIMO systems; spatio-temporal correlation; training signal design;
fLanguage :
English
Journal_Title :
Selected Topics in Signal Processing, IEEE Journal of
Publisher :
ieee
ISSN :
1932-4553
Type :
jour
DOI :
10.1109/JSTSP.2014.2327572
Filename :
6823657
Link To Document :
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