Regularized channel inversion with quantized feedback in down-link multiuser channels

Duckdong Hwang; Bruno Clerckx; Gil Kim

doi:10.1109/TWC.2009.12.090117

Regularized channel inversion with quantized feedback in down-link multiuser channels

Duckdong Hwang, Bruno Clerckx, Gil Kim

School of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

Abstract

We consider regularized channel inversion (RI) precoding in the multiuser multiple input multiple output (Mu- MIMO) broadcast channels. To make the RI operate in quantized channel feedback condition, we analyze the quantization error variance and its effect on the signal to interference plus noise power ratio (SINR) of the user signals. The regularization parameter α is optimized to balance the additive noise, the multiuser interference and the interference due to the quantization error and to maximize SINR. Analysis and simulations show that regularization enhances the performance of zero forcing beam forming (ZFBF) over the whole signal to noise power ratio (SNR) range. This is contrasting to the perfect channel status information (CSI) case, where the enhancement is observed in the low SNR region.

Original language	English
Article number	5351694
Pages (from-to)	5785-5789
Number of pages	5
Journal	IEEE Transactions on Wireless Communications
Volume	8
Issue number	12
DOIs	https://doi.org/10.1109/TWC.2009.12.090117
Publication status	Published - 2009 Dec

Keywords

Broadcasting channels
Quantized feedback
Regularized channel inversion
Sum-rate capacity

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering
Applied Mathematics

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10.1109/TWC.2009.12.090117

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abstract = "We consider regularized channel inversion (RI) precoding in the multiuser multiple input multiple output (Mu- MIMO) broadcast channels. To make the RI operate in quantized channel feedback condition, we analyze the quantization error variance and its effect on the signal to interference plus noise power ratio (SINR) of the user signals. The regularization parameter α is optimized to balance the additive noise, the multiuser interference and the interference due to the quantization error and to maximize SINR. Analysis and simulations show that regularization enhances the performance of zero forcing beam forming (ZFBF) over the whole signal to noise power ratio (SNR) range. This is contrasting to the perfect channel status information (CSI) case, where the enhancement is observed in the low SNR region.",

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N2 - We consider regularized channel inversion (RI) precoding in the multiuser multiple input multiple output (Mu- MIMO) broadcast channels. To make the RI operate in quantized channel feedback condition, we analyze the quantization error variance and its effect on the signal to interference plus noise power ratio (SINR) of the user signals. The regularization parameter α is optimized to balance the additive noise, the multiuser interference and the interference due to the quantization error and to maximize SINR. Analysis and simulations show that regularization enhances the performance of zero forcing beam forming (ZFBF) over the whole signal to noise power ratio (SNR) range. This is contrasting to the perfect channel status information (CSI) case, where the enhancement is observed in the low SNR region.

AB - We consider regularized channel inversion (RI) precoding in the multiuser multiple input multiple output (Mu- MIMO) broadcast channels. To make the RI operate in quantized channel feedback condition, we analyze the quantization error variance and its effect on the signal to interference plus noise power ratio (SINR) of the user signals. The regularization parameter α is optimized to balance the additive noise, the multiuser interference and the interference due to the quantization error and to maximize SINR. Analysis and simulations show that regularization enhances the performance of zero forcing beam forming (ZFBF) over the whole signal to noise power ratio (SNR) range. This is contrasting to the perfect channel status information (CSI) case, where the enhancement is observed in the low SNR region.

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KW - Sum-rate capacity

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Regularized channel inversion with quantized feedback in down-link multiuser channels

Abstract

Keywords

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