Proceedings. 2001 IEEE International Symposium on Information Theory (IEEE Cat. No.01CH37252)
DOI: 10.1109/isit.2001.936167
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Power control for the additive white Gaussian noise channel under channel estimation errors

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Cited by 29 publications
(28 citation statements)
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“…The latter is a variational problem, and we call μ Q (ŵ) the power control function, as in [13]. Said function imposes an average transmit power μ Q (ŵ) on the codebook to be used in conjunction with a specific channel estimateŵ.…”
Section: Problem Statementmentioning
confidence: 99%
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“…The latter is a variational problem, and we call μ Q (ŵ) the power control function, as in [13]. Said function imposes an average transmit power μ Q (ŵ) on the codebook to be used in conjunction with a specific channel estimateŵ.…”
Section: Problem Statementmentioning
confidence: 99%
“…Following ideas from [13], [4], we characterize the optimal power allocation strategy μ Q (ŵ) over time, which is the solution to the variational problem…”
Section: A Optimal Power Control Functionmentioning
confidence: 99%
“…Note that the mean square error of the channel estimation process can be further decreased by choosing the training signal S to minimize (9). In addition, it is stated in [11] that the training signal S primarily affects the achievable rate through the so called effective signal-to-noise ratio, which is shown to be inversely proportional to the MMSE [11].…”
Section: A Training and Channel Estimation Phasementioning
confidence: 99%
“…In [6] the authors characterized the Shannon capacity of a conventional single-input single-output (SISO) network under imperfect channel estimation. A related power allocation strategy that maximizes the achieved capacity of SISO with imperfect channel estimation has been proposed in [7]. The impact of an imperfect channel estimation on the capacity performance of a MIMO network as well as a related waterfilling power allocation (WF-PA) strategy have been proposed in [8].…”
Section: Introductionmentioning
confidence: 99%