Implementation of LS, MMSE and SAGE channel estimators for mobile MIMO-OFDM

The use of decision directed (DD) channel estimation in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) downlink receiver is studied in this paper. The 3GPP long term evolution (LTE) based pilot structure is used as a benchmark. The space-alternating generalized expectation-maximization (SAGE) algorithm is used to improve the performance from that of the pilot symbol based least-squares (LS) channel estimator. The DD channel estimation improves the performance with high user velocities, where the pilot symbol density is not sufficient. It can also be used to reduce the pilot overhead without any performance degradation. Minimum mean square error (MMSE) filtering can also be used in estimating the channel in between pilot symbols. The pilot based LS, MMSE and the SAGE channel estimators are implemented and the performance-complexity trade-offs are studied.

[1]  Markku J. Juntti,et al.  Space-time channel estimation and performance analysis for wireless MIMO-OFDM systems with spatial correlation , 2005, IEEE Transactions on Vehicular Technology.

[2]  Johan Eilert,et al.  Implementation Aspects of Channel Estimation for 3GPP LTE Terminals , 2011, EW.

[3]  Joseph R. Cavallaro,et al.  Performance—Complexity Comparison of Receivers for a LTE MIMO–OFDM System , 2010, IEEE Transactions on Signal Processing.

[4]  Markku J. Juntti,et al.  Avoiding Matrix Inversion in DD SAGE Channel Estimation in MIMO-OFDM with M-QAM , 2009, 2009 IEEE 70th Vehicular Technology Conference Fall.

[5]  Joseph R. Cavallaro,et al.  Configurable and scalable high throughput turbo decoder architecture for multiple 4G wireless standards , 2008, 2008 International Conference on Application-Specific Systems, Architectures and Processors.

[6]  Terng-Yin Hsu,et al.  Adaptive Frequency-Domain Channel Estimator in 4$ \,\times \,$4 MIMO-OFDM Modems , 2009, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[7]  Sailes K. Sengijpta.  Fundamentals of Statistical Signal Processing: Estimation Theory , 1995 .

[8]  Dionysios I. Reisis,et al.  A High Performance VLSI FFT Architecture , 2006, 2006 13th IEEE International Conference on Electronics, Circuits and Systems.

[9]  Umberto Mengali,et al.  A comparison of pilot-aided channel estimation methods for OFDM systems , 2001, IEEE Trans. Signal Process..

[10]  Jari H. Iinatti,et al.  Stochastic modelling of Rice fading channels with temporal, spatial and spectral correlation , 2007, IET Commun..

[11]  Wai Ho Mow,et al.  A VLSI architecture of a K-best lattice decoding algorithm for MIMO channels , 2002, 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353).

[12]  S.K. Wilson,et al.  On channel estimation in OFDM systems , 1995, 1995 IEEE 45th Vehicular Technology Conference. Countdown to the Wireless Twenty-First Century.

[13]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[14]  Marc Moonen,et al.  Optimal training design for MIMO OFDM systems in mobile wireless channels , 2003, IEEE Trans. Signal Process..

[15]  Yongzhe Xie,et al.  Two EM-type channel estimation algorithms for OFDM with transmitter diversity , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.