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Blind multi-input-multi-output channel tracking using decision-directed maximum-likelihood estimation
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AI
DOI：10.1109/TVT.2007.891479.png)
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Abstract
En 中文
In this paper, a new channel-estimation algorithm based on maximum-likelihood (ML) algorithm for estimation and tracking of the multiple-input-multiple-output (MIMO) channels is presented. The ML algorithm presents the optimum estimation when the exact channel model is known. The derived channel-estimation algorithm is very efficient, with a computational complexity comparable to the least mean square and much lower than the recursive least squares and the Kalman algorithms. The proposed algorithm is analyzed, and the effect of the channel-tracking error is applied as a modifying component for the derived algorithm. The proposed algorithm is simulated for half- and full-rank flat-fading time-varying MIMO channels for the different values of f(D)T, E-b/N-0, and training lengths via Monte Carlo simulation technique. The minimum mean-square-error (mmse) joint detector is considered as the detection algorithm. The output of the mmse receiver is considered as the virtual training data in the blind mode of operation: the same as in the decision-directed algorithm. By various simulations, the bit error rate and the mse of tracking the proposed algorithm for different values of f(D)T, presenting the speed of channel variations, are evaluated and compared with the Kalman filtering approach. By simulating the proposed algorithm for different values of the training length, the minimum training length required for different channel conditions is extracted.
Keywords:
blind
channel tracking
decision-directed algorithm (DDA)
maximum likelihood (ML)
mean square error (mse)
minimum mse (mmse)
multiple-input multiple-output (MIMO)
training
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W
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