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Low Complexity Linear Channel Estimation for MIMO Communication Systems

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Abstract

Channel estimation is employed to get the current knowledge of channel states for an optimum detection in fading environments. In this paper, a new recursive multiple input multiple output (MIMO) channel estimation is proposed which is based on the recursive least square solution. The proposed recursive algorithm utilizes short training sequence on one hand and requires low computational complexity on the other hand. The algorithm is evaluated on a MIMO communication system through simulations. It is realized that the proposed algorithm provides fast convergence as compared to recursive least square (RLS) and robust variable forgetting factor RLS (RVFF-RLS) adaptive algorithms while utilizing lesser computational cost and provides independency on forgetting factor.

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References

  1. Prasad, R., & Mihovska, A. (2009). New horizons in mobile and wireless communications: Radio interfaces (Vol. 1). Boston: Artech House.

  2. El Gamal, Hesham, & Roger Hammons, A. R. (2003). On the design of algebraic space-time codes for MIMO block-fading channels. IEEE Transactions on Information Theory, 49(1), 151–163.

    Article  MathSciNet  MATH  Google Scholar 

  3. Amin, M. R., & Trapasiya, S. D. (2012). Space time coding scheme for MIMO system-literature survey. Procedia Engineering, 38, 3509–3517.

    Article  Google Scholar 

  4. Mohammed, S. K., Zaki, A., Chockalingam, A., & Rajan, B. S. (2009). High-rate spacetime coded large-MIMO systems: Low-complexity detection and channel estimation. IEEE Journal of Selected Topics in Signal Processing, 3(6), 958–974.

    Article  Google Scholar 

  5. Balanis, C. A. (2012). Antenna theory: Analysis and design. New York: Wiley.

    Google Scholar 

  6. Dokhanchi, H., & Falahati, A. (2010). Adaptive MIMO LS estimation technique via MSE criterion over a Markov channel model. In IEEE symposium on computers and communications (ISCC) (pp. 151–154), Riccione, Italy.

  7. Kashoob, M., & Zakharov, Y. (2016). Selective detection with adaptive channel estimation for MIMO OFDM. In IEEE signal processing workshop on sensor array and multichannel (SAM) (pp. 1–5), Rio de Janerio, Brazil.

  8. Gui, G., Xu, L., Shan, L., & Adachi, F. (2014). Adaptive MIMO channel estimation using sparse variable step-size NLMS algorithms. In IEEE international conference on communication systems (ICCS) (pp. 605–609), Macau, China.

  9. Liu, X., Wang, J., Li, Z., & Si, J. (2017). Soft-output MMSE MIMO detector under different channel estimation models. IET Communications, 11(2), 192–197.

    Article  Google Scholar 

  10. Tong, L. (1995). Blind sequence estimation. IEEE Transactions on Communications, 43(12), 2986–2994.

    Article  MATH  Google Scholar 

  11. Vosoughi, A., & Scaglione, A. (2003). Channel estimation for precoded MIMO systems. In IEEE workshop on statistical signal processing (pp. 442–445), St. Louis, Mo, USA.

  12. Sayed, A. H., & Kailath, T. (1994). A state-space approach to adaptive RLS filtering. IEEE Signal Processing Magazine, 11(3), 18–60.

    Article  Google Scholar 

  13. Zou, Y., Chan, S. C., & Ng, T. S. (2000). A recursive least M-estimate (RLM) adaptive filter for robust filtering in impulse noise. IEEE Signal Processing Letters, 7(11), 324–326.

    Article  Google Scholar 

  14. Bhotto, M. Z. A., & Antoniou, A. (2011). Robust recursive least-squares adaptive-filtering algorithm for impulsive-noise environments. IEEE Signal Processing Letters, 18(3), 185–188.

    Article  Google Scholar 

  15. Haykin, S. S. (2008). Adaptive filter theory. Englewood Cliffs, NJ: Prentice Hall.

    MATH  Google Scholar 

  16. Verhaegen, M., & Verdult, V. (2012). Filtering and system identification: A least squares approach. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  17. Kovacevic, B., Banjac, Z., & Milosavljevic, M. (2013). Adaptive digital filters. Berlin: Springer.

    Book  MATH  Google Scholar 

  18. Benveniste, A., & Basseville, M. (1984). Detection of abrupt changes in signals and dynamical systems: Some statistical aspects. Analysis and Optimization of Systems, 62, 143–155.

    Article  MathSciNet  MATH  Google Scholar 

  19. Song, S. (2003). Self-tunning adaptive algorithm and applications. Doctoral dissertation, Ph.D. Dissertation, Seoul National University, 1525. http://library.snu.ac.kr/Eng/DetailView.jsp?uid=11&cid=1112838.

  20. Karami, E., & Shiva, M. (2006). Decision-directed recursive least squares MIMO channels tracking. EURASIP Journal on Wireless Communications and Networking, 2006(2), 7–7.

    MATH  Google Scholar 

  21. Arablouei, R., & Doganay, K. (2011). Modified RLS algorithm with enhanced tracking capability for MIMO channel estimation. Electronics Letters, 47(19), 1101–1103.

    Article  Google Scholar 

  22. Akino, T. K. (2008). Optimum-weighted RLS channel estimation for rapid fading MIMO channels. IEEE Transactions on Wireless Communications, 7, 4248–4260.

    Article  Google Scholar 

  23. Yapici, Y., & Yilmaz, A. O. (2009). Low-complexity iterative channel estimation and tracking for time-varying multi-antenna systems. In 2009 IEEE 20th international symposium on personal, indoor and mobile radio communications (pp. 1317–1321), Tokyo, Japan.

  24. Moshavi, S., Kanterakis, E. G., & Schilling, D. L. (1996). Multistage linear receivers for DS-CDMA systems. International Journal of Wireless Information Networks, 3(1), 1–17.

    Article  Google Scholar 

  25. Honig, M. L., & Xiao, W. (2001). Performance of reduced-rank linear interference suppression. IEEE Transactions on Information Theory, 47(5), 1928–1946.

    Article  MathSciNet  MATH  Google Scholar 

  26. Sessler, G. M., & Jondral, F. K. (2005). Low complexity polynomial expansion multiuser detector for CDMA systems. IEEE Transactions on Vehicular Technology, 54(4), 1379–1391.

    Article  Google Scholar 

  27. Hoydis, J., Debbah, M., & Kobayashi, M. (2011). Asymptotic moments for interference mitigation in correlated fading channels. In 2011 IEEE international symposium on information theory proceedings (ISIT) (pp. 2796–2800), Saint Peterburg, Russia.

  28. Paleologu, C., Benesty, J., & Ciochina, S. (2008). A robust variable forgetting factor recursive least-squares algorithm for system identification. IEEE Signal Processing Letters, 15, 597–600.

    Article  Google Scholar 

  29. Jakes, W. C. (1974). Microwave mobile communications (3rd ed.). New York: Wiley.

    Google Scholar 

  30. Bello, P. (1963). Characterization of randomly time-variant linear channels. IEEE Transactions on Communications Systems, 11(4), 360–393.

    Article  Google Scholar 

  31. Tsatsanis, M. K., Giannakis, G. B., & Zhou, G. (1996). Estimation and equalization of fading channels with random coefficients. In IEEE international conference on acoustics, speech, and signal processing, Atlanta, GA, USA (vol. 2, pp. 1093–1096).

  32. Weikert, O. E. (2007). Blinde Demodulation in MIMO-Ubertragungssystemen, Ph.D. dissertation, Helmut Schmidt University/University of the Federal Armed Forces Hamburg, dissertation.de, ISBN 978-3-86624-273-9.

  33. Liu, Z., Ma, X., & Giannakis, G. B. (2002). Space-time coding and Kalman filtering for time-selective fading channels. IEEE Transactions on Communications, 50(2), 183–186.

    Article  Google Scholar 

  34. Komninakis, C., Fragouli, C., Sayed, A. H., & Wesel, R. D. (2002). Multi-input multi-output fading channel tracking and equalization using Kalman estimation. IEEE Transactions on Signal Processing, 50(5), 1065–1076.

    Article  Google Scholar 

  35. Yano, K., & Yoshida, S. (2005). CDMA non-linear interference canceller with multi-beam reception. In Fifth international conference on information, communications and signal processing (pp. 6–10), Bangkok, Thailand.

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Correspondence to Hasan Raza.

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Raza, H., Khan, N.M. Low Complexity Linear Channel Estimation for MIMO Communication Systems. Wireless Pers Commun 97, 5031–5044 (2017). https://doi.org/10.1007/s11277-017-4763-5

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