Performance Analysis of MIMO Cellular Network with Channel Estimation Errors

Wen, Chao-Kai; Chen, Jung-Chieh; Ting, Pangan; Hsiao, Cheng-Yuen; Jian, Yung-Yih; Hsu, Jen-Yuan

doi:10.1109/vtcf.2006.177

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Cited by 2 publications

(2 citation statements)

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“…As α increases, the MSE w.r.t. H does not vanish as expected and converges to a constant corresponding to mse X2 = 0 in (24). Presumably, we may improve the channel estimation quality by increasing β 1 .…”

Section: Discussion and Numerical Resultssupporting

confidence: 52%

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Performance limits of massive MIMO systems based on Bayes-optimal inference

Wen¹,

Wu²,

Wong³

et al. 2015

2015 IEEE International Conference on Communications (ICC)

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Abstract-This paper gives a replica analysis for the minimum mean square error (MSE) of a massive multiple-input multipleoutput (MIMO) system by using Bayesian inference. The Bayesoptimal estimator is adopted to estimate the data symbols and the channels from a block of received signals in the spatial-temporal domain. We show that using the Bayes-optimal estimator, the interfering signals from adjacent cells can be separated from the received signals without pilot information. In addition, the MSEs with respect to the data symbols and the channels of the desired users decrease with the number of receive antennas and the number of data symbols, respectively. There are no residual interference terms that remain bounded away from zero as the numbers of receive antennas and data symbols approach infinity.

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Section: Discussion and Numerical Resultssupporting

confidence: 52%

“…If the channel estimate along with its error statistic is used for data estimation under the Bayes-optimal approach, we can get the MSE w.r.t. X 2 by applying the methods in [23,24]…”

Section: Discussion and Numerical Resultsmentioning

confidence: 99%