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Lattice-reduction-aided MMSE precoding for correlated MIMO channels and performance analysis 被引量:1

Lattice-reduction-aided MMSE precoding for correlated MIMO channels and performance analysis
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摘要 The lattice-reduction (LR) has been developed to im- prove the performance of the zero-forcing (ZF) precoder in multiple input multiple output (MIMO) systems. Under the assumptions of uncorrelated flat fading channel model and perfect channel state information at the transmitter (CSIT), an LR-aided ZF precoder is able to collect the full transmit diversity. With the complex Lenstra- Lenstra-Lov^sz (LLL) algorithm and limited feedforward structure, an LR-aided linear minimum-mean-square-error (LMMSE) pre- coder for spatial correlated MIMO channels and imperfect CSIT is proposed to achieve lower bit error rate (BER). Assuming a time division duplexing (TDD) MIMO system, correlated block flat fad- ing channel and LMMSE uplink channel estimator, it is proved that the proposed LR-aided LMMSE precoder can also obtain the full transmit diversity through an analytical approach. Furthermore, the simulation results show that with the quadrature phase shift keying (QPSK) modulation at the transmitter, the uncoded and coded BERs of the LR-aided LMMSE precoder are lower than that of the traditional LMMSE precoder respectively when Eb-No is greater than 10 dB and 12 dB at all correlation coefficients. The lattice-reduction (LR) has been developed to im- prove the performance of the zero-forcing (ZF) precoder in multiple input multiple output (MIMO) systems. Under the assumptions of uncorrelated flat fading channel model and perfect channel state information at the transmitter (CSIT), an LR-aided ZF precoder is able to collect the full transmit diversity. With the complex Lenstra- Lenstra-Lov^sz (LLL) algorithm and limited feedforward structure, an LR-aided linear minimum-mean-square-error (LMMSE) pre- coder for spatial correlated MIMO channels and imperfect CSIT is proposed to achieve lower bit error rate (BER). Assuming a time division duplexing (TDD) MIMO system, correlated block flat fad- ing channel and LMMSE uplink channel estimator, it is proved that the proposed LR-aided LMMSE precoder can also obtain the full transmit diversity through an analytical approach. Furthermore, the simulation results show that with the quadrature phase shift keying (QPSK) modulation at the transmitter, the uncoded and coded BERs of the LR-aided LMMSE precoder are lower than that of the traditional LMMSE precoder respectively when Eb-No is greater than 10 dB and 12 dB at all correlation coefficients.
出处 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第1期16-23,共8页 系统工程与电子技术(英文版)
基金 supported by the National Science Fund for Distinguished Young Scholars (60725105) the National Basic Research Program of China (2009CB320404) the Program for Changjiang Scholars and Innovative Research Team in University (IRT0852) the 111 Project(B08038) the National Natural Science Foundation of China (60702057) the Special Research Fund of State Key Laboratory (ISN1102003) the National Science and Technology Major Project (2011ZX03001-007-01)
关键词 multiple input multiple output (MIMO) lattice reduction correlated channel linear precoding. multiple input multiple output (MIMO), lattice reduction, correlated channel, linear precoding.
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  • 1P. W. Wolniansky, G. J. Foschini, G. D. Golden, et al. V-BLAST: an architecture for realizing very high data rates over the rich- scattering wireless channel. Proc. of the URSI International Symposium on Signals, Systems, and Electronics, 1998: 295– 300.
  • 2H. Yao, G. Wornell. Lattice-reduction-aided detectors for MIMO communication systems. Proc. of the IEEE Global Communications Conference, 2002: 424–428.
  • 3D. Wu¨bben, R. Bo¨hnke, V. Ku¨hn, et al. Near maximum- likelihood detection of MIMO systems using MMSE-based lat- tice reduction. Proc. of the IEEE International Conference on Communications, 2004: 798–802.
  • 4M. Taherzadeh, A. Mobasher, A. K. Khandani. LLL reduction achieves the receiver diversity in MIMO decoding. IEEE Trans. on Information Theory, 2007, 53(12): 4801–4805.
  • 5X. Ma, W. Zhang. Performance analysis for MIMO systems with lattice-reduction aided linear equalization. IEEE Trans. on Communications, 2008, 56(2): 309–318.
  • 6C. Windpassinger, R. F. H. Fischer. Low-complexity near maximum-likelihood detection and precoding for MIMO sys- tems using lattice reduction. Proc. of the Information Theory Workshop, 2003: 345–348.
  • 7C. Stierstorfer, R. F. H. Fischer. Lattice-reduction-aided Tomlinson–Harashima precoding for point-to-multipoint trans- mission. International Journal of Electronics and Communica- tions (AEu¨), 2006, 60(4): 328–330.
  • 8J. P. Kermoal, L. Schumacher, K. Ingemann, et al. A stochas- tic MIMO radio channel model with experimental validiation. IEEE Journal on Selected Areas in Communications, 2002, 20(6): 1211–1226.
  • 9A. van Zelst, J. Hammerschmidt. A single coefficient spatial correlation model for multiple-input multiple-output (MIMO) radio channel. Proc. of the General Assembly of URSI, 2002: 1–4.
  • 10T. Yoo, A. J. Goldsmith. Capacity and power allocation for fad- ing MIMO channels with channel estimation error. IEEE Trans. on Information Theory, 2006, 52(5): 2203–2214.

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