Joint MMSE vector precoding based on GMD method for MIMO systems

Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, Shanghai Shi, China
IEICE Transactions on Communications (Impact Factor: 0.33). 09/2007; E90B(9). DOI: 10.1093/ietcom/e90-b.9.2617
Source: OAI

ABSTRACT We propose a geometric mean decomposition (GMD) based vector precoding (VP) for multiple input multiple output (MIMO) systems. Minimum mean square error (MMSE) criterion is used for the joint VP design. The application of GMD method eliminates the imbalance among subchannel gains and obtains a better perturbation vector than the conventional method. We then exploit the extended channel matrix for further performance improvement. Simulation results show the proposed schemes significantly outperform the existing VP schemes.

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    ABSTRACT: Uniform channel decomposition (UCD), as an improvement of geometric mean decomposition (GMD), is capable of decomposing a multiple-input-multiple-output (MIMO) channel into multiple subchannels having an identical capacity. Vector precoding (VP) is a powerful scheme, which is capable of mitigating multiuser interference (MUI) at the transmitter, provided that the channels of the users are known. In this paper, a novel joint transceiver design based on the UCD and the minimum bit error rate (MBER) VP principles is proposed for MIMO systems, in which the precoding and equalization matrices are calculated by UCD, whereas the perturbation vector is chosen to minimize the system's bit error ratio (BER). This UCD-MBER-VP transceiver design outperforms several state-of-the-art benchmark algorithms in terms of achievable BER performance without imposing an increased computational complexity, particularly for rank-deficient systems.
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    ABSTRACT: Block diagonalization (BD) algorithm is a generalization of the channel inversion that converts multiuser multi-input multi-output (MIMO) broadcast channel into single-user MIMO channel without inter-user interference. In this paper, we combine the BD technique with a minimum mean square error vector precoding (MMSE-VP) for achieving further gain in performance with minimal computational overhead. Two key ingredients to make our approach effective are the QR decomposition based block diagonalization and joint optimization of transmitter and receiver parameters in the MMSE sense. In fact, by optimizing precoded signal vector and perturbation vector in the transmitter and receiver jointly, we pursue an optimal balance between the residual interference mitigation and the noise enhancement suppression. From the sum rate analysis as well as the bit error rate simulations (both uncoded and coded cases) in realistic multiuser MIMO downlink, we show that the proposed BD-MVP brings substantial performance gain over existing multiuser MIMO algorithms.
    Proceedings of IEEE International Conference on Communications, ICC 2011, Kyoto, Japan, 5-9 June, 2011; 06/2011

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