Non-Linear Quantization for Lattice Reduction Aided MIMO Detection

doi:10.13190/jbupt.201001.18.218

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2010, Vol. 33 ›› Issue (1): 18-22.doi: 10.13190/jbupt.201001.18.218

• Papers • Previous Articles Next Articles

Non-Linear Quantization for Lattice Reduction Aided MIMO Detection

SUN Yan-hua¹;WANG Hao²;ZHANG Yan-hua¹

(1.School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China; 2.Potevio Institute of Technology Company Limited, Beijing 100080, China)

Received:2009-05-04 Revised:2009-11-18 Online:2010-02-28 Published:2010-02-28

Abstract

Abstract:

Aiming at the high complexity of non-linear combined quantization caused by constellation biased distribution in lattice reduction aided(LRA) multiple input multiple output (MIMO) detection, a sorted feedback non-linear quantization scheme is proposed. It considers the border of transformed constellation and the correlations between elements, meanwhile, the element is selected to be quantized according to the value of the quantization errors. Simulation shows that the performance of LRA MIMO detection based on this quantization scheme is superior to the performance of LRA MIMO detection based on non-sorted feedback and independent element quantization. Furthermore, the LRA MIMO detection based on the proposed scheme can approach the performance of maximum likelihood detection in polynomial complexity in a flat fading channel.

Key words: multiple input multiple output, lattice reduction, detection algorithm, non-linear quantization

SUN Yan-hua¹;WANG Hao²;ZHANG Yan-hua¹. Non-Linear Quantization for Lattice Reduction Aided MIMO Detection[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2010, 33(1): 18-22.

References

[1] Telatar I E. Capacity of multi-antenna Gaussian channels. Eur Trans Telecom, 1999, 10(6): 585-595.
[2] Wolniansky P W, Foschini G J, Golden G D, et al. V-BLAST: an architecture for realizing very high data rates over the rich-scattering wireless channel//ISSSE98. Pisa: IEEE Press, 1998: 230-235.
[3] Ahmed D K, Amir S, Jean P C, et al. New list sphere decoding and iterative synchronization algorithms for MIMO-OFDM detection with LDPC FEC[J]. IEEE Trans on Vehicular Technology, 2008, 57(6): 3510-3524.
[4] Yang Yijin, Zhao Chunming, Zhou Peng, et al. MIMO detection of 16QAM signaling based on semidefinite relaxation[J]. IEEE Trans on Signal Processing, 2007, 14(11): 797-799.
[5] Yao Huan, Wornell G W. Lattice-reduction-aided detectors for MIMO communication systems//GLOBECOM02. Taiwan: IEEE Press, 2002: 424-428.
[6] Windpassinger C, Fischer R F H. Low-complexity near-maximum-likelihood detection and precoding for MIMO systems using lattice reduction//IEEE Information Theory Workshop. Paris: IEEE Press, 2003: 324-327.
[7] Wubben D, Bohnke R, Kuhn V, et al. Near maximum likelihood detection of MIMO systems using MMSE-based lattice reduction//ICC 2004. Paris: IEEE Press, 2004: 798-802.
[8] Lee D J, Kim R W, Byun Y S. Improved lattice reduction aided detection based on extended noise variance matrix for MIMO systems//SPAWC 2007. Helsinki: IEEE Press, 2007: 1-5.
[9] Park S B, Jong J S, Lee D J, et al. List LRAD based on semi-definite relaxation for MIMO systems//ICACT 2008. Korea: IEEE Press, 2008: 1196-1199.
[10] Nguyen T D, Fujino T, Tran X N. Quantization error correction scheme for lattice-reduction aided linear detection in MIMO systems//WIMOB 2007. New York: IEEE Press, 2007: 11-16.
[11] Schnoor C P, Euchner M. Lattice basis reduction improved practical algorithms and solving subsets sum problems[J]. Mathematical Programming, 1994, 66(2): 181-199.
[12] Lenstra A K, Lenstra H W, Lovasz L. Factoring polynomials with rational coefficients[J]. Math Ann, 1982, 261(4): 515-534.
[13] Sandell M, Lillie A, McNamara D, et al. Complexity study of lattice reduction for MIMO detection//WCNC 2007. Hong Kong: IEEE Press, 2007: 1089-1092.

[1]	. Joint SNR estimation and modulation recognition of MIMO-OFDM signals based on multitask learning [J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(6): 98-104.
[2]	ZHENG Feng, YANG Li. Joint Beamforming Scheme Based on PSO Algorithm for RIS-MIMO System [J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(5): 23-29.
[3]	ZHU Jun, TANG Bao-yu, LI Kai. Prediction Algorithm of Mobile User Channel Capacity Based on Dynamic Mode Decomposition [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(4): 89-94.
[4]	ZHAO Wei, WANG Bin, BAO Hui, ZHAO Pin-fang, LI Bao-gang. Energy Efficient Resource Allocation for Secure MEC System Based on mMIMO [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(6): 126-131.
[5]	TANG Jie, LI Kai, LIN Chu-ting, SONG Yan, ZHOU En-cheng. The Research on Channel Capacity of OAM-MIMO System [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(6): 27-35.
[6]	JIN Xiao-ping, WU Qing, JIN Ning, CHEN Dong-xiao, WANG Jia-tian. Detection and Performance Analysis Based on Quadrature Index Modulation Multiple Access [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(4): 120-126.
[7]	ZHU Jiang, LIANG Jing-wen, Lü Zhi-qiang. A Multiuser Interference Cancellation Algorithm in Time Reversal Division Multiple Access System [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(3): 59-65.
[8]	DING Qing-feng, DING Xu, LIN Zhi-ming. A Low Complexity Detection Algorithm Based on the Weight Factors for Spatial Modulation System [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(2): 31-35.
[9]	OU Ze-liang, YANG Hong-wen. An Adaptive Massive MIMO Signal Detection Algorithm Based on Expectation Propagation [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(5): 149-152.
[10]	LIU Guang-yi, ZHENG Yi, WANG Fei, LIU Jian-jun, WANG Qi-xing. Latest Progress on 5G NR Trial of Enhanced Mobile Broad Band [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(5): 44-51.
[11]	LAN Xian-gui, CAO Jie, LIAO Yong. Robust Double-Layer Precoding Algorithm for Multi-Cell Downlink MIMO Based on Partial Collaboration [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(2): 92-96.
[12]	HUANG Miao-na, CHEN Jun, REN Bin. Joint Power Control and Beamforming Optimization for MIMO-OFDM Network [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(1): 139-144.
[13]	LI Yi-wei, TANG Xuan, LIN Bang-jiang, ZHANG Shi-hao. Channel Characteristics of Dual-Polarized MIMO System for Indoor Scenario [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2016, 39(6): 53-56.
[14]	LI Xing-wang, AI Xiao-yu, ZHANG Yan-qin, LI Li-hua, LU Guang-yan. Performance Analysis of 3D MU-MIMO Systems over K Composite Fading Channels [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2016, 39(5): 56-60.
[15]	QU Hua, LIANG Jing, ZHAO Ji-hong, WANG Wei-hua. Low-Complexity Detection Based on Classical Iterative Method for LS-MIMO Systems [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2016, 39(4): 83-86.

Non-Linear Quantization for Lattice Reduction Aided MIMO Detection

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments