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联合ML和OSIC的格规约辅助检测算法

Lattice Reduction Aided Detection Combined ML and OSIC
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摘要 为了提升高信噪比情况下次优检测算法的检测性能,在一层遍历搜索的排序串行干扰消除算法(ML-OSIC)的基础上引入格规约技术,提出一种新的格规约辅助检测算法(ML-LLL-OSIC).该算法首先对噪声干扰最小的子信道信号进行遍历检测,然后利用格规约技术处理剩余子信道矩阵以改善信道矩阵的正交性,最后利用改进的信道矩阵进行排序串行干扰抵消检测.上述算法既减少了遍历检测搜索次数,又改善了信道矩阵的正交性,可更加有效地避免层间干扰和误码扩散.仿真结果表明,在多用户多入多出系统(MU-MIMO)中,QPSK调制方式下信噪比大于10d B时,或16QAM调制方式下信噪比大于14d B时,本算法可以获得接近ML算法的检测性能. In order to improve the detection performance of suboptimal detection algorithm in the high SNR,This paper introduces the lattice technology based on ML-OSIC, and presents a new lattice reduction aided detection algorithm ( ML-LLL-OSIC ). The algorithm first traverses the best noise detection sub-channel signals, then the lattice processing the remaining channel to improves the orthogonal- ity of the channel matrix,finally the OSIC algorithm on the basis of new channel matrix. This algorithm reduces search times, while improves the orthogonality of the channel matrix and more effectively avoids the interference between layers,and error diffusion. The simulation results show that the algorithm can get the same performance as ML detection algorithm in MU-MIMO system when the SNR is greater than 10dB in QPSK modulation mode or the SNR is greater than 14dB in 16QAM modulation mode.
出处 《小型微型计算机系统》 CSCD 北大核心 2015年第4期764-767,共4页 Journal of Chinese Computer Systems
基金 国家自然科学基金项目(61074175 61203160)资助 河北省自然基金项目(F2014201168)资助
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  • 1张庆荣,王刚,孙宇昊,李道本.LR算法在MIMO-LAS-CDMA系统中的应用[J].吉林大学学报(信息科学版),2006,24(1):12-17. 被引量:3
  • 2P. W. Wolniansky, G. J. Foschini, G. D. Golden, and R. A. Valenzuela. V-BLAST: an architecture for realizing very high data rates over the rich-scattering wireless channel, in Proc. IEEE ISSSE, 1998:295-300.
  • 3G. J. Foschini, G. D. Golden, R. A. Valenzuela, and P. W. Wolniansky. Simplified processing for high spectral efficiency wireless communication employing multi-element arrays. IEEE J. Sel. Areas Commun. , Nov. 1999, 17 (9) : 1841-1852.
  • 4He Guanghui, Yu Wei, Zhon Zucheng. Reduced Complexity Detection for V-BLAST Systems:from Theory to Practice. 2006 International Conference on Communications, Circuits and Systems Proceedings, Volume 4, June 2006: 2549- 2553.
  • 5Kyungchuh Lee, Kyungchun Lee. Symbol detection in VBLAST architectures under channel estimation errors. IEEE Transactions on Wireless Communications, Feb. 2007,6 (2) :593-597.
  • 6R. Van Nee, A. van Zelst, G. Awater. Maximum likelihood decoding in a space division multiplexing system. Proc. of IEEE Vehicular Technology Conference (VTC) 2000, Tokyo ,2000:6-10.
  • 7G. D. Golden, G. J. Foschini, R. A. Valenzuela, P. W. Wolniansky. Detection Algorithm and Initial Laboratory Resuits Using V-BLAST Space-Time Communi-cation Architecture. Electronics Letters, 1999,35 ( 1 ) : 11-14.
  • 8Won-Joon Choi, Rohit Negi, and John M. Cioffi. Combined ML and DFE decoding for the V-BLAST System. Proc. IEEE ICC'00,2000 : 1243-1248.
  • 9Qianlei Liu, Luxi Yang. A Simplified Method for V-BLAST Detection in MIMO OFDM Communication. 10th Asia-Pacific Conference on Communications and 5th International Symposium on Multi-Dimensional Mobile Communications, 2004.
  • 10L Giangaspero, L Agarossi, G Pahenghi. Co-channel interference cancellation based on MIMO-OFDM systems. IEEE Wireless Communications,2002,9(6) :8-17.

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