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A Novel LowComplexity Channel Estimation Approach for Single Carrier MIMO Frequency Selective Channels
Suyue LI Jian XIONG Lin GUI Youyun XU Baoyu ZHENG
Publication
IEICE TRANSACTIONS on Communications
Vol.E96B
No.1
pp.233241 Publication Date: 2013/01/01
Online ISSN: 17451345
DOI: 10.1587/transcom.E96.B.233
Print ISSN: 09168516 Type of Manuscript: PAPER Category: Wireless Communication Technologies Keyword: correlation channel estimation, joint ISI and CCI cancellation, MIMO training sequences, LSTMMSE frequency domain equalization,
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Summary:
A simple yet effective time domain correlation channel estimation method is proposed for multipleinput multipleoutput (MIMO) systems over dispersive channels. It is known that the inherent cochannel interference (CCI) and intersymbol interference (ISI) coexist when the signals propagate through MIMO frequency selective channels, which renders the MIMO channel estimation intractable. By elaborately devising the quasiorthogonal training sequences between multiple antennas which have constant autocorrelation property with different cyclic shifts in the time domain, the interferences induced by ISI and CCI can be simultaneously maintained at a constant and identical value under quasistatic channels. As a consequence, it is advisable to implement the joint ISI and CCI cancelation by solving the constructed linear equation on the basis of the correlation output with optional correlation window. Finally, a general and simplified closedform expression of the estimated channel impulse response can be acquired without matrix inversion. Additionally, the layered spacetime (LST) minimum mean square error (MMSE) (LSTMMSE) frequency domain equalization is briefly described. We also provide some meaningful discussions on the beginning index of the variable correlation window and on the cyclic shift number of msequence of other antennas relative to the first antenna. Simulation results demonstrate that the proposed channel estimation approach apparently outperforms the existing schemes with a remarkable reduction in computational complexity.

