Abstract
Channel estimation algorithms and their implementations for mobile receivers are considered in this paper. The 3GPP long term evolution (LTE) based pilot structure is used as a benchmark in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) receiver. The decision directed (DD) 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 performance is improved with high user velocities, where the pilot symbol density is not sufficient. Minimum mean square error (MMSE) filtering is also used in estimating the channel in between pilot symbols. The pilot overhead can be reduced to a third of the LTE pilot overhead with DD channel estimation, obtaining a ten percent increase in data throughput. Complexity reduction and latency issues are considered in the architecture design. The pilot based LS, MMSE and the SAGE channel estimators are implemented with a high level synthesis tool, synthesized with the UMC 0.18 \(\mu \)m CMOS technology and the performance-complexity trade-offs are studied. The MMSE estimator improves the performance from the simple LS estimator with LTE pilot structure and has low power consumption. The SAGE estimator has high power consumption but can be used with reduced pilot density to increase the data rate.
Similar content being viewed by others
References
3rd Generation Partnership Project (3GPP); Technical Specification Group Radio Access Network. (2010). Evolved universal terrestrial radio access E-UTRA; physical channels and modulation (release 10) TS 36.211 (version 10.0.0). Technical Report.
Cavers, J.K. (1991). An analysis of pilot symbol assisted modulation for Rayleigh fading channels. IEEE Transactions on Vehicular Technology, 40(4), 686–693.
Kay, S.M. (1993). Fundamentals of statistical signal processing: estimation theory. Englewood Cliffs: Prentice-Hall.
Morelli, M., & Mengali, U. (2001). A comparison of pilot-aided channel estimation methods for OFDM systems. IEEE Transactions on Signal Processing, 49(12), 3065–3073.
Barhumi, I., Leus, G., Moonen, M. (2003). Optimal training design for MIMO–OFDM systems in mobile wireless channels. IEEE Transactions on Signal Processing, 51(6), 1615–1624.
Dempster, A.P., Laird, N.M., Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39(1), 1–38.
Fessler, J., & Hero, A. (1994). Space-alternating generalized expectation-maximization algorithm. IEEE Transactions on Signal Processing, 42(10), 2664–2677.
Xie, Y., & Georghiades, C.N. (2003). Two EM–type channel estimation algorithms for OFDM with transmitter diversity. IEEE Transactions on Communications, 51(1), 106–115.
Panayirci, E., Şenol, H., Poor, H. (2010). Joint channel estimation, equalization, and data detection for OFDM systems in the presence of very high mobility. IEEE Transactions on Signal Processing, 58(8), 4225–4238.
Ylioinas, J., & Juntti, M. (2009). Iterative joint detection, decoding, and channel estimation in turbo coded MIMO-OFDM. IEEE Transactions on Vehicular Technology, 58(4), 1784–1796.
Li, Y., Cimini, L.J., Sollenberger, N.R. (1998). Robust channel estimation for OFDM systems with rapid dispersive fading channels. IEEE Transactions on Communications, 46(7), 902–915.
Miao, H., & Juntti, M. (2005). Space-time channel estimation and performance analysis for wireless MIMO-OFDM systems with spatial correlation. Transactions on Vehicular Technology, 54(6), 2003–2016.
Simko, M., Wu, D., Mehlfuehrer, C., Eilert, J., Liu, D. (2011). Implementation aspects of channel estimation for 3GPP LTE terminals. In Proceedings of the European wireless conference, April 27–29. Vienna.
Sun, M.-F., Juan, T.-Y., Lin, K.-S., Hsu, T.-Y. (2009). Adaptive frequency-domain channel estimator in 4 × 4 MIMO-OFDM modems. IEEE Transactions on VLSI Systems, 17(11), 1616–1625.
Chen, H.-Y., Ku, M.-L., Jou, S.-J., Huang, C.-C. (2010). A robust channel estimator for high-mobility STBC-OFDM systems. IEEE Transactions on Circuits Systems I, 57(4), 925–936.
Ketonen, J., Juntti, M., Ylioinas, J. (2010). Decision directed channel estimation for reducing pilot overhead in LTE-A. In Proceedings of the annual asilomar conference on signals, systems, and computers. Nov. 7–10 (pp. 1503–1507). Pacific Grove: IEEE.
Ylioinas, J., Raghavendra, M.R., Juntti, M. (2009). Avoiding matrix inversion in DD SAGE channel estimation in MIMOOFDM with M-QAM. In Proceedings of the IEEE vehicular technology conference. Sept. 20–23 (pp. 1–5). Anchorage: IEEE.
3rd Generation Partnership Project (3GPP); Technical Specification Group Radio Access Network. (2003). Spatial channel model for multiple input multiple output (MIMO) simulations (3G TS 25.996 version 6.0.0 (release 6)). 3rd Generation Partnership Project (3GPP), Tech. Rep.
Kunnari, E., & Iinatti, J. (2007). Stochastic modelling of Rice fading channels with temporal, spatial and spectral correlation. IET Communications, 1(2), 215–224.
Wong, K., Tsui, C., Cheng, R.K., Mow, W. (2002). A VLSI architecture of a K-best lattice decoding algorithm for MIMO channels. In Proceedings of the IEEE international symposium on circuits and systems. May 26–29 (Vol. 3, pp. 273–276). Scottsdale: IEEE.
Lee, J., Lou, H.-L., Toumpakaris, D., Cioffi, J. (2006). SNR analysis of OFDM systems in the presence of carrier frequency offset for fading channels. IEEE Transactions on Wireless Communications, 5(12), 3360–3364.
Ketonen, J., Juntti, M., Cavallaro, J. (2010). Performance-complexity comparison of receivers for a LTE MIMO-OFDM system. IEEE Transactions on Signal Processing, 58(6), 3360–3372.
Martin, G., & Smith, G. (2009). High-level synthesis: past, present, and future. IEEE Design Test of Computers, 26(4), 18–25.
Calypto. (2013). Catapult overview. Tech. Rep. http://calypto.com/en/products/catapult/overview.
Sun, Y., Zhu, Y., Goel, M., Cavallaro, J. (2008). Configurable and scalable high throughput turbo decoder architecture for multiple 4G wireless standards. In Proceedings of the IEEE international conference on application-specific systems, architectures and processors (ASAP). July 2–4 (pp. 209–214). Leuven: IEEE.
Babionitakis, K., Manolopoulos, K., Nakos, K., Chouliaras, V. (2006). A high performance VLSI FFT architecture. In Proceedings of the IEEE international conference on electronics, circuits and systems December 10–13 (pp. 810–813). Nice: IEEE.
Acknowledgments
The authors would like to thank Calypto and Mentor Graphics for the possibility to use the Catapult C \({{\circledR }}\) Synthesis tool.
Author information
Authors and Affiliations
Corresponding author
Additional information
This research has been supported in part by Tekes, the Finnish Funding Agency for Technology and Innovation, Nokia Siemens Networks, Renesas Mobile Europe, Elektrobit, Xilinx and Academy of Finland as well as the Nokia Foundation. The Rice University co-author was supported in part by the NSF under grants CNS-1265332, ECCS-1232274, and EECS-0925942.
Rights and permissions
About this article
Cite this article
Ketonen, J., Juntti, M., Ylioinas, J. et al. Decision-Directed Channel Estimation Implementation for Spectral Efficiency Improvement in Mobile MIMO-OFDM. J Sign Process Syst 79, 233–245 (2015). https://doi.org/10.1007/s11265-013-0833-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11265-013-0833-4