On the effect of channel estimation error with superimposed training upon information rates

Author(s):  
A. Vosoughi ◽  
A. Scaglione
2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
O. Longoria-Gandara ◽  
R. Parra-Michel ◽  
M. Bazdresch ◽  
A. G. Orozco-Lugo

This contribution describes a novel iterative radio channel estimation algorithm based on superimposed training (ST) estimation technique. The proposed algorithm draws an analogy with the data dependent ST (DDST) algorithm, that is, extracts the cycling mean of the data, but in this case at the receiver's end. We first demonstrate that this mean removal ST (MRST) applied to estimate a single-input single-output (SISO) wideband channel results in similar bit error rate (BER) performance in comparison with other iterative techniques, but with less complexity. Subsequently, we jointly use the MRST and Alamouti coding to obtain an estimate of the multiple-input multiple-output (MIMO) narrowband radio channel. The impact of imperfect channel on the BER performance is evidenced by a comparison between the MRST method and the best iterative techniques found in the literature. The proposed algorithm shows a good tradeoff performance between complexity, channel estimation error, and noise immunity.


2009 ◽  
Vol E92-B (5) ◽  
pp. 1553-1562
Author(s):  
Takashi ISOGAI ◽  
Mamoru SAWAHASHI ◽  
Hidekazu TAOKA ◽  
Kenichi HIGUCHI

2013 ◽  
Vol 62 (9) ◽  
pp. 4666-4672 ◽  
Author(s):  
Dongkyu Kim ◽  
Hyungsik Ju ◽  
Sungsoo Park ◽  
Daesik Hong

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