Login



Other Articles by Author(s)

Huazhang Lv.
Jianping Li



Author(s) and WSEAS

Huazhang Lv.
Jianping Li


WSEAS Transactions on Communications


Print ISSN: 1109-2742
E-ISSN: 2224-2864

Volume 16, 2017

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.



A Multiplex Transmission Scheme Based on Lattice Ruduction Decoding for MIMO System

AUTHORS: Huazhang Lv., Jianping Li

Download as PDF

ABSTRACT: Lenstra-Lenstra-Lovász (LLL) is an effective lattice reduction algorithm in multi-input-multi-output (MIMO) system. The use of LLL lattice reduction can significantly improve the performance of zero-forcing and successive interference cancellation decoders in MIMO communications.But for conventional single channel transmission, when the data streams go through poor channel condition, it usually need higher transmission signal noise ratio(SNR) at transmit side to overcome the poor condition and guarantee better receiving performance at receive side.This may be a challenge for designing transmitter and receiver.In this paper, we introduce a multiplex transmission scheme based on LLL algorithm that transmits the same bit streams for multiple times.By means of appling LLL detection and hard decision majority logic decoding to the multi-channel streams of data, the final detection results will reach a lower bit error rate(BER) level at the same SNR. So it needs lower transmit power compared to original single channel transmission. Performance gain will be obtained by this multiplex transmission and majority logic decoding

KEYWORDS: LLL Algorithm, Multiplex Transmission, Majority Logical Decoding, MIMO System

REFERENCES:

[1] Panzner.B.Zirwas, W. Dierks.S, Lauridsen. M, Mogensen, P.Pajukoski and K,Deshan Miao,” Deployment and implementation strategies for massive MIMO in 5G,” Globecom Workshops (GC Wkshps), pp. 346 – 351,2014

[2] F. Boccardi, R. W. Heath Jr, A. Lozano, T. L. Marzetta, and P. Popovski,“Five disruptive technology directions for 5G,”IEEE Commun. Mag., vol. 52, no. 2, pp. 74–80, Feb. 2014.

[3] W.H.Mow, “Maximum likelihood sequence estimation from the lattice viewpoint,” IEEE Trans. Inf. Theory, vol. 40, pp. 1591–1600, Sept. 1994.

[4] E. Viterbo and J. Boutros, “A universal lattice code decoder for fading channels,” IEEE Trans. Inf. Theory, vol. 45, pp. 1639–1642, July 1999.

[5] H. Yao and G. W. Wornell, “Lattice-reductionaided detectors for MIMO communication systems,” in Proc. Globecom’02, Taipei, China, Nov. 2002, pp. 17–21.

[6] M.Taherzadeh, A.Mobasher and A.K.Khandani,”LLL Reduction Achieves the Receive Diversity in MIMO Decoding,” IEEE Trans. Inf. Theory, vol.53, pp.4801-4805, December 2007.

[7] C. Ling, “Towards characterizing the performance of approximate lattice decoding in MIMO communications,” in Proc. International Symposium on Turbo Codes, Munich, Germany, Apr. 3-7, 2006.

[8] Y. H. Gan and W. H. Mow, “Complex lattice reduction algorithms for low-complexity MIMO detection,” in Proc. IEEE Global Telecommun. Conf. (IEEE GLOBECOM ’05), 2005, vol. 5, pp. 5–5.

[9] M. Taherzadeh, A. Mobasher, and A. K. Khandani, “Lattice-basis reduction achieves the precoding diversity in MIMO broadcast systems,” in Proc. 39th Conf. on Information Sciences and Systems. Johns Hopkins Univ., USA, Mar. 15-18, 2005.

[10] Ma X, Zhang W, “Performance analysis for MIMO systems with lattice-reduction aided linear equalization,” IEEE Trans Commun 56:309–318, 2008.

[11] C. Ling. H. Mow, and N. Howgrave-Graham, “Variants of the LLL algorithm in digital communications: Complexity analysis and fixed complexity implementation,” IEEE Trans. Inf. Theory, 2007.

[12] C. Ling and N. Howgrave-Graham, “Effective LLL reduction for lattice decoding,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Nice, France, Jun. 2007.

[13] Gan YH, Ling C, Mow WH, “Complex lattice reduction algorithm for low-complexity fulldiversity MIMO detection.” IEEE Trans Signal Process 57:2701–2710. 2009.

[14] Wen Zhang, Sanzheng Qiao, and Yimin Wei,”The Diagonal Reduction Algorithm Using Fast Givens.” To appear in Proceedings of the 10th Asian Symposium on Computer Mathematics, Oct. 26-28, 2012, Beijing

[15] K. Zhao, Y. Li, H.Jiang and S. Du, “A Low Complexity Fast Lattice Reduction Algorithm for MIMO Detection,” in PIMRC 2013, 2013, pp. 1612–1616.

[16] Q. Wen and X. Ma, “An Efficient Greedy LLL Algorithm for MIMO Detection,” in MILCOM 2014, 2014, pp. 550–555.

[17] Wen Zhang, Sanzheng Qiao, and Yimin Wei. HKZ and Minkowski,”Reduction Algorithms for Lattice-Reduction-Aided MIMO Detection.” IEEE Transactions on Signal Processing. Vol. 60, No. 11, 2012.

[18] C. P. Schnorr, “Progress on LLL and lattice reduction,” in LLL+25 Conf., Caen, France, Jun. 2007.

[19] L.Bai and J.Choi,”Low-Complexity-MIMO Detection.” Springer Science Business Media, LLC 2012.

[20] Murray R. Bremner, Lattice Basis Reduction: An Introduction to the LLL Algorithm and Its Applications. Taylor & Francis Group, LLC, 2012.

WSEAS Transactions on Communications, ISSN / E-ISSN: 1109-2742 / 2224-2864, Volume 16, 2017, Art. #43, pp. 380-387


Copyright © 2017 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

Bulletin Board

Currently:

The editorial board is accepting papers.


WSEAS Main Site