• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3A
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• pp.260-268
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• 2010

Channel inversion is one of the simplest techniques for multiuser downlink systems with single-antenna users. In this paper, we extend the regularized channel inversion technique developed for the single-antenna user case to multiuser multiple-input multiple-output (MIMO) channels with multiple-antenna users. We first employ the multiuser preprocessing to project the multiuser signals near the null space of the unintended users based on the MMSE criterion, and then the single-user preprocessing is applied to the decomposed MIMO interference channels. In order to reduce the complexity, we focus on non-iterative solutions for the multiuser transmit beamforming and use a linear receiver based on an MMSE criterion. Simulation results show that the proposed scheme outperforms existing joint iterative algorithms in most multiuser configurations.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.5
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• pp.11-20
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• 2002

Based on regularization principles, we propose a new dequantization scheme on DCT-based transform coding for reducing of blocking artifacts and minimizing the quantization error. The conventional image dequantization is simply to multiply the received quantized DCT coefficients by the quantization matrix. Therefore, for each DCT coefficients, we premise that the quantization noise is as large as half quantizer step size (in DCT domain). Our approach is based on basic constraint that quantization error is bounded to ${\pm}$(quantizer spacing/2) and at least there are not high frequency components corresponding to discontinuities across block boundaries of the images. Through regularization, our proposed dequantization scheme, sharply reduces blocking artifacts in decoded images. Our proposed algorithm guarantees that the dequantization process will map the quantized DCT coefficients will be evaluated against the standard JPEG, MPEG-1 and H.263 (with Annex J deblocking filter) decoding process. The experimental results will show visual improvements as well as numerical improvements in terms of the peak-signal-to-noise ratio (PSNR) and the blockiness measure (BM) to be defined.