Sorted QR-Decomposition Based Parallel Detection Algorithm for MIMO Systems

Abstract:

Multiple input multiple output (MIMO) wireless communication system can increase system capacity enormously. Maximum likelihood (ML) detection algorithm can obtain the optimal detection performance with exponential computational complexity that results it difficulty to use in practice. Classical ordered successive interference cancellation (SIC) algorithm suffers from error propagation and high complexity, so an improved parallel SIC algorithm based on Maximum likelihood (ML) detection is proposed, in which signal detection is performed at two stages. ML detections for one layer is carried out firstly, and redundancy of candidate sequences are selected to perform parallel detection for improving detection performance for next step. Sorted QR decomposition based SIC algorithm are performed in second step in order to reduce calculating complexity. By adjusting the number of candidate sequences, tradeoff between detection performance and calculating complexity can be obtained properly.

• Periodical: Applied Mechanics and Materials (Volumes 427-429)
• Pages: 591-595
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.427-429.591
• Online since: September 2013
• Authors: Li Liu, Jin Kuan Wang, Xin Song, Ying Hua Han, Dong Mei Yan
• Keywords: MIMO, Ml Detection, Silicon Carbide (SiC), Sorted QR Decomposition

References

[1] G. J. Foschini, M. Gans, On limits of wireless communications in a fading environment when using multiple antennas, Wireless Personal Communications, vol. 6, no. 3, pp.311-335, (1998).

DOI: 10.1023/a:1008889222784

[2] E. Telatar, Capacity of multi-antenna Gaussian channel, Europe Trans. Telecommun., vol. 10, no. 11, pp.585-595, (1999).

DOI: 10.1002/ett.4460100604

[3] C. W. Zheng, X. Z. Chu, J. McAllister,; R. Woods, Real-Valued Fixed-Complexity Sphere Decoder for High Dimensional QAM-MIMO Systems,. IEEE Transactions on Signal Processing, vol. 59, no. 9, pp.4493-4499, (2011).

DOI: 10.1109/tsp.2011.2159213

[4] C. E. Chen, W. H. Sheen, A New Lattice Reduction Algorithm for LR-Aided MIMO Linear Detection, IEEE Transactions on Wireless Communications, vol. 10, no. 8, pp.2417-2422, (2011).

DOI: 10.1109/twc.2011.061311.101297

[5] J. Shen, M. Q. Wu, L. L. Luan, Grover's Quantum Search Based Data Detection Algorithm for MIMO System, International Journal of Advancements in Computing Technology, vol. 4, no. 1, pp.344-350, (2012).

[6] Y. G. Jia, C. Andrieu, R. J. Piechocki, M. Sandell, Depth-First and Breadth-First Search Based Multilevel SGA Algorithms for Near Optimal Symbol Detection in MIMO Systems, IEEE Transactions on Wireless Communications, vol. 7, no. 3, pp.1052-1061, (2008).

DOI: 10.1109/twc.2008.060813

[7] H. G. Kang,I. Song, J. Oh, J. Lee, S. Yoon, Breadth-First Signal Decoder: A Novel Maximum-Likelihood Scheme for Multi-Input–Multi-Output Systems, IEEE Transactions on Vehicular Technology, vol. 57, no. 3, pp.1576-1584, (2008).

DOI: 10.1109/tvt.2007.909246

[8] D.A. Basnayaka, P.J. Smith, P.A. Martin, The Effect of Macrodiversity on the Performance of MLD in Flat Rayleigh/Rician Fading, IEEE Communications Letters, vol. 16, no. 11, pp.1764-1767, (2012).

DOI: 10.1109/lcomm.2012.092812.121327

[9] A.I. Sulyman, Y. Zahrani, S. Dosari, A. Sanie, S. Shebeili, V. Tarokh, Two-stage constellation partition algorithm for reduced-complexity multiple-input multiple-output-maximum-likelihood detection systems, IET Communications, vol. 6, no. 18, pp.3350-3357, (2012).

DOI: 10.1049/iet-com.2012.0173

[10] Y. Jiang, M.K. Varanasi, J. Li, Performance Analysis of ZF and MMSE Equalizers for MIMO Systems: An In-Depth Study of the High SNR Regime, IEEE Transactions on Information Theory, vol. 57, no. 4, pp.2008-2026, (2011).

DOI: 10.1109/tit.2011.2112070

[11] J. S. Chen; X. L. Yu, ZF V-BLAST for Imperfect MIMO Channels using Average Performance Optimization, IEEE International Conference on Acoustics, Speech and Signal Processing, pp. III-141-144, (2007).

DOI: 10.1109/icassp.2007.366492

[12] G. J. Foschini, Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas, Bell Labs. Tech. J., vol. 1, pp.41-59, (1996).

DOI: 10.1002/bltj.2015

[13] J. Chen; J. Liang; Y. X. Tang; S. Q. Li, A Low Complexity Near Maximum Likelihood VBLAST Algorithm for MIMO Systems, Wireless Communications and Networking Conference, pp.1078-1082, (2007).

DOI: 10.1109/wcnc.2007.204

[14] H. Kwon, J. Lee, I. Kang, Successive Interference Cancellation via Rank-Reduced Maximum A Posteriori Detection, IEEE Transactions on Communications, wol. 61, no. 2, pp.628-637, (2013).

DOI: 10.1109/tcomm.2012.122112.120165