接近最优检测性能的低复杂度线性并行MIMO检测算法

doi:10.3969/j.issn.1000-436x.2013.02.002

Abstract

Abstract:

The existing parallel detection algorithms for MIMO systems have extremely high complexity under high order QAM and its complexity grows rapidly with the increase of the number of antenna.The non-parallel detection algorithms with low complexity have great SNR gap with optimal detection algorithm.A new parallel linear detection algorithm with low complexity was proposed.This algorithm exploited all symbols in QAM constellation as a reference to cancel the interference of the worst SNR sub-channel and detected the received signals of the rest with a new parallel linear detection algorithm based on lattice reduction.The simulation results show that the proposed detection algorithm is near the performance of the optimal detection algorithm and improves the diversity order with low complexity.Especially under the high order QAM, the complexity is decreased in evidence.

Key words: MIMO system, parallel detection, lattice reduction, QAM

Guo-sheng RUI,Hai-bo ZHANG,Yang ZHANG,Wen-biao TIAN,Song ZHANG,Ting-jun LI. Low complexity linear parallel detection algorithm for near ML detection of MIMO systems[J]. Journal on Communications, 2013, 34(2): 8-14.

Figures/Tables 9

步骤	复数乘法次数	复数加法次数
Step 1	$3 / 2 N_{T}^{2} N_{R} - 1 / 2 N_{T} N_{R}$	$3 / 2 N_{T}^{2} N_{R} - 1 / 2 N_{T}^{2} - N_{T} N_{R}$
Step 2	N_RM	(N_R ?1)M
Step 3	(N_T?1)³ N_R log_a(N_T?1)
Step 4	3/2(N_T ?1)²N_R?1/2(N_T?1)N_R	3/2(N_T?1)²N_R?1/2(N_T?1)²?(N_T?1)N_R
	(N_T?1)N_RM	(N_T?1)(N_R?1)M
	(N_T?1)(N_T?1)M	(N_T?1)(N_T?2)M
	(N_T?1)(N_T?1)M	(N_T?1)(N_T?2)M +N_RM
Step 5	1/2MN_R	1/2M(N_R ?1)

References 13

[1]	PROAKIS J G , SALEHI M . Digital Communications[M]. New York: McGraw-Hill, 2007.
[2]	WUBBEN D , SEETHALER D , JALDEN J , et al. Lattice reduction[J]. IEEE Signal Processing Magazine, 2011,28(3): 70-91.
[3]	WON-JOON C , NEGI R , CIOFFI J M . Combined ML and DFE decoding for the V-BLAST system[A]. Communications ICC 2000.2000 IEEE International Conference[C]. New Orleans, LA, USA, 2000.1243-1248.
[4]	WATERS D W , BARRY J R . Noise-predictive decision-feedback detection for multiple-input multiple-output channels[J]. IEEE Transactions on Signal Processing, 2005,53(5): 1852-1859.
[5]	倪兴, 王晓湘, 杜娟 . 一种新的基于噪声预测的部分判决反馈MIMO接收算法[J]. 电子与信息学报, 2008,30(1): 52-54. NI X , WANG X X , DU J . A noise-predictive partial decision-feedback detection for MIMO systems[J]. Journal of Electronics ＆ Information Technology, 2008,30(1): 52-54.
[6]	LUO Z , ZHAO M , LIU S , et al. Generalized parallel interference cancellation with near-optimal detection performance[J]. Signal Processing IEEE Transactions, 2008,56(1): 304-312.
[7]	STUDER C , FATEH S , SEETHALER D . ASIC implementation of soft-input soft-output MIMO detection using MMSE parallel interference cancellation[J]. IEEE Journal of Solid-State Circuits, 2011,46(7): 1754-1765.
[8]	BREMNER M R . Lattice Basis Reduction[M]. New York: CRC Press, 2011.55-77.
[9]	CHO Y S , KIM J , YANG W Y , et al. MIMO-OFDM Wireless Communications with MATLAB[M]. Singapore: Wiley, 2011.320-322.
[10]	HUNG G , LING C , HO M . Complex lattice reduction algorithm for low-complexity full-diversity MIMO detection[J]. IEEE Transactions on Signal Processing, 2009,57(7): 2701-2710.
[11]	AUBERT S , MOHAISEN M . From linear equalization to latticereduction-aided sphere-detector as an answer to the MIMO detection problematic in spatial multiplexing systems[J]. Vehicular Technologies:Increasing Connectivity, 2011,4(4): 71-96.
[12]	WUBBEN D , BOHNKE R , KUHN V , et al. MMSE-based lattice-reduction for near-ML detection of MIMO systems[A]. Smart Antennas, 2004 ITG Workshop[C]. Berlin, Germany, 2004.106-113.
[13]	CHEN C , SHEEN W . A new lattice reduction algorithm for LR-aided MIMO linear detection[J]. IEEE Transactions on Wireless Communications, 2011,10(8): 2417-2422.

Metrics

Recommended 0

No Suggested Reading articles found!

算法	运行时间/s
ZF	72.271 9
MMSE	73.047 1
LR ZF	105.273 9
LR MMSE	106.213 3
本文算法	129.832 2
NP(1)	138.996 2
GPIC(1,0)	116.089 7
GPIC(1,1)	202.454 5

[1]	Yuan HUANG, Yigang HE, Yuting WU, Tongtong CHENG, Yongbo SUI, Shuguang NING. Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systems [J]. Journal on Communications, 2021, 42(8): 61-69.
[2]	Rui WANG, Min SHEN, Yun HE, Xiangyan LIU. Research on caching strategy based on transmission delay in Cell-Free massive MIMO systems [J]. Journal on Communications, 2021, 42(12): 134-143.
[3]	Huadi ZHANG,Huaxun LOU. Automatic modulation recognition algorithm for MQAM signal [J]. Journal on Communications, 2019, 40(8): 200-211.
[4]	Guoquan LI,Xiangyun ZHOU,Jinzhao LIN,Yongjun XU,Yu PANG,Jiacheng WANG. Full-diversity design criterion for multi-user STBC based on integer-forcing detection [J]. Journal on Communications, 2019, 40(3): 139-146.
[5]	Weina YUAN,Qiu YAN. Channel estimation method based on compressive sensing for FBMC/OQAM system [J]. Journal on Communications, 2019, 40(12): 98-104.
[6]	Xian ZHOU,Tianyu GAO,Jiahao HUO,Xiaojie SHEN,Dongxu LU,Wei HUANGFU,Jiajing TU,Keping LONG. Research on IQ imbalance estimation algorithm based on GSOP [J]. Journal on Communications, 2018, 39(9): 129-134.
[7]	Wen-jie PENG,Yue-heng LI,Mei-yan JU,Ping HUANG,Guo-ping TAN. Downlink outage probability analysis of distributed MIMO systems over a composite fading channel [J]. Journal on Communications, 2014, 35(6): 161-169.
[8]	. Downlink outage probability analysis of distributed MIMO systems over a composite fading channel [J]. Journal on Communications, 2014, 35(6): 21-168.
[9]	Zhao LI,Jian-dong LI,Qin LIU,Biao SHEN. Interference alignment based spectrum sharing and user scheduling for cognitive radio MIMO system [J]. Journal on Communications, 2014, 35(1): 167-172.
[10]	Zhi-jin ZHAO,Lin CHEN,Hai-quan WANG,Lei SHEN. Blind recognition of real orthogonal STBC based on independent component analysis [J]. Journal on Communications, 2012, 33(11): 1-7.
[11]	Sheng-qian HAN,Chen-yang YANG. Power allocation under per-antenna power constraints in multiuser MIMO systems [J]. Journal on Communications, 2012, 33(10): 71-76.
[12]	Bin WANG,Lin-dong GE,Di WU,Fu-qiang XUE. Blind equalization algorithm for high-order QAM signals [J]. Journal on Communications, 2010, 31(8A): 10-16.
[13]	Meng ZHOU,Can ZHANG. Design scheme of linear dispersive space-time codes for downlink multiuser MIMO systems [J]. Journal on Communications, 2010, 31(1): 135-140.
[14]	Yuan JING,Fu-liang YIN. Wishart random matrix based Bayesian estimation for time-varying channel in the color noise [J]. Journal on Communications, 2009, 30(2): 79-84.
[15]	Chen ZHANG,Yao-wen FU,Er-yang ZHANG. Research on PAPR reduction scheme in QAM-OFDM satellite communication system [J]. Journal on Communications, 2006, 27(8): 67-72.

Low complexity linear parallel detection algorithm for near ML detection of MIMO systems

RichHTML

PDF

Knowledge

Cited

Abstract

Cite this article

share this article

Figures/Tables 9

References 13

Related Articles 15

Metrics

Recommended 0