TRAINING SIGNAL DESIGN FOR CHANNEL ESTIMATION IN MASSIVE MIMO SYSTEMS

被引：0
|
作者：
Noh, Song [1 ]
Zoltowski, Michael D. [1 ]
Sung, Youngchul
Love, David J. [1 ]
机构：
[1] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2014年
关键词：
Channel estimation; Kalman filtering; Gauss-Markov model; massive MIMO; WIRELESS; DOWNLINK;
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
In this paper, the design of training signals for channel estimation in massive multiple-input multiple-output (MIMO) systems is considered. Under a stationary, block Gauss-Markov channel model, a method for optimal pilot beam pattern design for enhanced channel estimation is proposed, exploiting both the properties of Kalman filtering and the spatio-temporal channel correlation. First, pilot beam pattern design is considered under the assumption of orthogonal beam patterns within a block. The orthogonality assumption is subsequently relaxed and the design problem is solved via a greedy approach. Numerical results show the efficacy of the proposed algorithm.
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页数：5
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