Browse > Article
http://dx.doi.org/10.3837/tiis.2016.11.006

ML Symbol Detection for MIMO Systems in the Presence of Channel Estimation Errors  

Yoo, Namsik (Department of Electrical and Information Engineering, Seoul National University of Science and Technology)
Back, Jong-Hyen (Signaling & Communication Team, Metropolitan Transportation Research Center, Korea Railroad Research Institute)
Choi, Hyeon-Yeong (Signaling & Communication Team, Metropolitan Transportation Research Center, Korea Railroad Research Institute)
Lee, Kyungchun (Department of Electrical and Information Engineering, Seoul National University of Science and Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.10, no.11, 2016 , pp. 5305-5321 More about this Journal
Abstract
In wireless communication, the multiple-input multiple-output (MIMO) system is a well-known approach to improve the reliability as well as the data rate. In MIMO systems, channel state information (CSI) is typically required at the receiver to detect transmitted signals; however, in practical systems, the CSI is imperfect and contains errors, which affect the overall system performance. In this paper, we propose a novel maximum likelihood (ML) scheme for MIMO systems that is robust to the CSI errors. We apply an optimization method to estimate an instantaneous covariance matrix of the CSI errors in order to improve the detection performance. Furthermore, we propose the employment of the list sphere decoding (LSD) scheme to reduce the computational complexity, which is capable of efficiently finding a reduced set of the candidate symbol vectors for the computation of the covariance matrix of the CSI errors. An iterative detection scheme is also proposed to further improve the detection performance.
Keywords
MIMO; Maximum Likelihood Detection; List Sphere Decoding; Channel Estimation Error; Channel State Information;
Citations & Related Records
연도 인용수 순위
  • Reference
1 M. Biguesh and A. B. Gershman, "Training-based MIMO channel estimation: A study of estimator tradeoffs and optimal training signals," IEEE Transactions on Signal Processing, vol.54, no.3, pp.884-893, 2006.   DOI
2 S. Shahbazpanahi, A. B. Gershman, and J. H.Manton, "Closed-form blind MIMO channel estimation for orthogonal space-time block codes," IEEE Transactions on Signal Processing, vol.53, no.12, pp.4506-4517, 2005.   DOI
3 D. Qu, G. Zhu, and T. Jiang, "Training Sequence Design and Parameter Estimation of MIMO Channels with Carrier Frequency Offsets," IEEE Transactions on Wireless Communications, vol.5, no.12, pp.3662-3666, Dec. 2006.   DOI
4 D. Kong, D. Qu, K. Luo, and T. Jiang, "Channel Estimation under Staggered Frame Structure for Massive MIMO system," IEEE Transactions on Wireless Communications, vol.15, no.2, pp.1469-1479, 2016.   DOI
5 J. L. Melsa and D. L. Cohn, Decision and Estimation Theory, McGraw-Hill, 1978.
6 V. Trees and Harry L, Detection, Estimation, and Modulation theory, John Wiley & Sons, 2004.
7 A. A. Farhoodi and M. Biguesh, "Robust ML detection algorithm for MIMO receivers in presence of channel estimation error," in Proc. of 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, pp.1-5, 2006.
8 B. S. Thian and A. Goldsmith, "Reduced-complexity robust MIMO decoders," IEEE Transactions on Wireless Communications, vol.12, no.8, pp.3783-3795, 2013.   DOI
9 G. Taricco and E. Biglieri, "Space-time decoding with imperfect channel estimation," IEEE Transactions on Wireless Communications, vol.4, no.4, pp.1874-1888, 2005.   DOI
10 Z. Zhou and B. Vucetic, "Design of adaptive modulation using imperfect CSI in MIMO systems," Electronics Letters, vol.40, no.17, pp.1073-1075, 2004.   DOI
11 B. Hassibi and H. Vikalo, "On the sphere-decoding algorithm I. Expected complexity," IEEE Transactions on Signal Processing, vol.53, no.8, pp.2806-2818, 2005.   DOI
12 R.-R. Chen, R. Koetter, U. Madhow, and D. Agrawal, "Joint noncoherent demodulation and decoding for block fading channel: A practical framework for approaching Shannon capacity," IEEE Transactions on Communication, vol. 51, no. 10, pp. 1676-1689, Oct. 2003.   DOI
13 M.Medard and D. N. C. Tse, "Spreading in block-fading channels," in Proc. 34th Asilomar Conference on Signals, Systems and Computers, vol.2, pp.1598-1602, Oct. 2000.
14 S. I. Resnick, A Probability Path, Springer Science & Business Media, 2013.
15 B. M. Hochwald and S. ten Brink, "Achieving near-capacity on a multiple-antenna channel," IEEE Transactions on Communications, vol.51, no.3, pp.389-399, 2003.   DOI
16 J. Jalden and B. Ottersten, "On the complexity of sphere decoding in digital communications," IEEE Transactions on Signal Processing, vol.53, no.4, pp.1474-1484, 2005.   DOI
17 L. Zheng and D. N. C. Tse, "Diversity and multiplexing: A fundamental tradeoff in multiple-antenna channels," IEEE Transactions on Information Theory, vol.49, no.5, pp.1073-1096, 2003.   DOI
18 B. Hassibi and B. M. Hochwald, "How much training is needed in multiple-antenna wireless links?" IEEE Transactions on Information Theory, vol.49, no.4, pp.951-963, 2003.   DOI
19 T. L. Marzetta, "BLAST training: estimating channel characteristics for high-capacity space-time wireless," in Proc. of 37th Annual Allerton Conference Communications, Control, and Computing, vol. 37, pp. 958-966, 1999.