On Superimposed Training for MIMO Channel Estimation and Symbol Detection

2007 ◽  
Vol 55 (6) ◽  
pp. 3007-3021 ◽  
Author(s):  
Shuangchi He ◽  
Jitendra K. Tugnait ◽  
Xiaohong Meng
Keyword(s):  
Channel Estimation ◽  
Mimo Channel ◽  
Superimposed Training ◽  
Symbol Detection

Channel Estimation and Symbol Detection with ICI Cancellation Based on Superimposed Training for OFDM Systems

2012 ◽  
Vol E95.B (9) ◽  
pp. 2926-2930
Author(s):  
Qinjuan ZHANG ◽  
Muqing WU ◽  
Qilin GUO ◽  
Rui ZHANG ◽  
Chao Yi ZHANG
Keyword(s):  
Channel Estimation ◽  
Ofdm Systems ◽  
Superimposed Training ◽  
Symbol Detection ◽  
Ici Cancellation

scholarly journals Superimposed Training-Based Channel Estimation for MIMO Relay Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaoyan Xu ◽  
Jianjun Wu ◽  
Shubo Ren ◽  
Lingyang Song ◽  
Haige Xiang
Keyword(s):  
Channel Estimation ◽  
Multiple Input Multiple Output ◽  
Estimation Algorithm ◽  
Relay Network ◽  
Mimo Channel ◽  
Amplify And Forward ◽  
Error Performance ◽  
Superimposed Training ◽  
Training Strategy ◽  
The Individual

We introduce the superimposed training strategy into the multiple-input multiple-output (MIMO) amplify-and-forward (AF) one-way relay network (OWRN) to perform the individual channel estimation at the destination. Through the superposition of a group of additional training vectors at the relay subject to power allocation, the separated estimates of the source-relay and relay-destination channels can be obtained directly at the destination, and the accordance with the two-hop AF strategy can be guaranteed at the same time. The closed-form Bayesian Cramér-Rao lower bound (CRLB) is derived for the estimation of two sets of flat-fading MIMO channel under random channel parameters and further exploited to design the optimal training vectors. A specific suboptimal channel estimation algorithm is applied in the MIMO AF OWRN using the optimal training sequences, and the normalized mean square error performance for the estimation is provided to verify the Bayesian CRLB results.

scholarly journals Iterative Mean Removal Superimposed Training for SISO and MIMO Channel Estimation

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
O. Longoria-Gandara ◽  
R. Parra-Michel ◽  
M. Bazdresch ◽  
A. G. Orozco-Lugo
Keyword(s):  
Channel Estimation ◽  
Radio Channel ◽  
Estimation Error ◽  
Mimo Channel ◽  
Channel Estimation Error ◽  
Superimposed Training ◽  
Iterative Techniques ◽  
Alamouti Coding ◽  
The Impact ◽  
Ber Performance

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.

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