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Polynomial Expansion-Based MMSE Channel Estimation and Precoding for Massive MIMO-GFDM Systems

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Abstract

In this paper, low-complexity channel estimators and precoders are proposed for massive multiple-input multiple-output generalized frequency division multiplexing (MIMO-GFDM) systems. In order to combat the effect of non-orthogonality in GFDM, interference-free pilots are used in frequency-domain minimum mean square error (MMSE) channel estimation. Polynomial expansion is used to approximately compute the matrix inverses in the conventional MMSE estimation and precoding, consequently reducing the cubic computational complexity to square order. The degree of the matrix polynomial can be properly selected to get a required trade-off between complexity and estimation/precoding performance. Different weights can be assigned to the terms in the polynomial expansion and be optimized to achieve a minimal mean square error (MSE). Derived limits on the MSE of the proposed estimators can predict their performance in the high \(E_s/N_0\) region. Then, we derive a Cramér-Rao lower bound (CRLB) and use it as a benchmark for the estimators. In addition, the related computational complexity and the impacts of the polynomial degree are also investigated. Numerical results show the accuracy of the proposed channel estimators and precoders.

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References

  1. Fettweis, G., Krondorf, M., & Bittner, S. (2009). In IEEE 69th Vehicular Technology Conference (VTC Spring). Barcelona, Spain.

  2. Michailow, N., Matthé, M., Gaspar, I. S., Caldevilla, A. N., Mendes, L. L., Festag, A., & Fettweis, G. (2014). Generalized frequency division multiplexing for 5th generation cellular network. IEEE Transactions on Communications, 62(9), 3045–3061.

    Article  Google Scholar 

  3. Wunder, G., Jung, P., Kasparick, M., Wild, T., Schaich, F., Chen, Y., et al. (2014). 5GNOW: Non-orthogonal, asynchronous waveforms for future mobile applications. IEEE Communications Magazine, 52(2), 97–105.

    Article  Google Scholar 

  4. Nimr, A., Li, Z., Chafii, M., & Fettweis, G. (2011). In Radio Access Network Slicing and Virtualization for 5G Vertical Industries. (Chap. 4, pp. 63–82). 2021, Wiley. https://doi.org/10.1002/9781119652434.ch4.

  5. Fettweis, G. P. (2014). The tactile internet: Applications and challenges. IEEE Vehicular Technology Magazine, 9(1), 64–70.

    Article  Google Scholar 

  6. Liang, Y. C., Chen, K. C., Li, G. Y., & Mahonen, P. (2011). Cognative radio networking and communications: An overview. IEEE Transactions on Vehicular Technology, 60(7), 3386–3407.

    Article  Google Scholar 

  7. Matthé, M., Zhang, D., & Fettweis, G. (2016). In 22th European Wireless Conference. Finland, Finland: Oulu.

  8. Zhang, D., Mendes, L. L., Matthé, M., Gaspar, I. M., Michailow, N., & Fettweis, G. (2016). Expectation propagation for near-optimum detection of MIMO-GFDM signals. IEEE Transactions on Wireless Communications, 15(2), 1045–1062.

    Article  Google Scholar 

  9. Khan, M. Q., & Nisar, M. D. (2021). In 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring). Finland: Helsinki.

  10. Larsson, E. G., Edfors, O., Tufvesson, F., & Marzetta, T. L. (2014). Massive MIMO for next generation wireless systems. IEEE Communications Magazine, 52(2), 186–195.

    Article  Google Scholar 

  11. Lu, L., Li, G. Y., Swindlehurst, A. L., Ashikhmin, A., & Zhang, R. (2014). An overview of massive MIMO: Benefits and challenges. IEEE Journal of Selected Topics in Signal Processing, 8(5), 742–758.

    Article  Google Scholar 

  12. de Figueiredo, F. A. P. (2022). An overview of massive MIMO for 5G and 6G. IEEE Latin America Transactions, 20(6), 931–940.

    Article  Google Scholar 

  13. Elhoushy, S., Ibrahim, M., & Hamouda, W. (2022). Cell-free massive MIMO: A survey. IEEE Communications Surveys & Tutorials, 24(1), 492–523.

    Article  Google Scholar 

  14. Yin, H., Gesbert, D., Filippou, M., & Liu, Y. (2013). A coordinated approach to channel estimation in large-scale multiple-antenna systems. IEEE Journal on Selected Areas in Communications, 31(2), 264–273.

    Article  Google Scholar 

  15. Liu, Y., Wong, T. F., & Hager, W. W. (2007). Training signal design for estimation of correlated MIMO channels with colored interference. IEEE Transactions on Signal Processing, 55(4), 1486–1497.

    Article  MathSciNet  MATH  Google Scholar 

  16. Ehsanfar, S., Matthé, M., Zhang, D., & Fettweis, G. (2016). In 20th International ITG Workshop on Smart Antennas (WSA 2016). Germany: Munich.

  17. Ehsanfar, S., Matthé, M., Zhang, D., & Fettweis, G. (2016). In 2016 IEEE Global Communications Conference (GLOBECOM). Washington: DC, USA.

  18. Ehsanfar, S., Matthé, M., Zhang, D., & Fettweis, G. (2017). In 2017 IEEE Wireless Communications and Networking Conference (WCNC). San Francisco: CA, USA.

  19. Shariati, N., Wang, J., & Bengtsson, M. (2014). Robust training sequence design for correlated MIMO channel estimation. IEEE Transactions on Signal Processing, 62(1), 107–120.

    Article  MathSciNet  MATH  Google Scholar 

  20. 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 

  21. Josse, N. L., Laot, C., & Amis, K. (2008). Efficient series expansion for matrix inversion with application to MMSE equalization. IEEE Communications Letters, 12(1), 35–37.

    Article  Google Scholar 

  22. Shariati, N., Björnson, E., Bengtsson, M., & Debbah, M. (2014). Low-complexity polynomial channel estimation in large-scale MIMO with arbitrary statistics. IEEE Journal of Selected Topics in Signal Processing, 8(5), 815–830.

    Article  Google Scholar 

  23. Elijah, O., Leow, C. Y., Rahman, T. A., Nunoo, S., & Iliya, S. Z. (2016). A comprehensive survey of pilot contamination in Massive MIMO-5G system. IEEE Communications Surveys & Tutorials, 18(2), 905–923.

    Article  Google Scholar 

  24. Albreem, M. A., Habbash, A. H. A., Abu-Hudrouss, A. M., & Ikki, S. S. (2021). Overview of precoding techniques for massive MIMO. IEEE Access, 9, 60764–60801.

    Article  Google Scholar 

  25. Costa, M. (1983). Writing on dirty paper. IEEE Transactions on Information Theory, 29(3), 439–441.

    Article  MathSciNet  MATH  Google Scholar 

  26. Windpassinger, C., Fischer, R. F. H., & Huber, J. B. (2004). Lattice-reduction-aided broadcast precoding. IEEE Transactions on Communications, 52(12), 2057–2060.

    Article  Google Scholar 

  27. Hochwald, B. M., Peel, C. B., & Swindlehurst, A. L. (2005). A vector-perturbation technique for near-capacity multiantenna multiuser communication-part II: Perturbation. IEEE Transactions on Communications, 53(3), 537–544.

    Article  Google Scholar 

  28. Rusek, F., Persson, D., Lau, B. K., Larsson, E. G., Marzetta, T. L., Edfors, O., & Tufvesson, F. (2013). Scaling up MIMO: Opportunities and challenges with very large arrays. IEEE Signal Processing Magazine, 30(1), 40–60.

    Article  Google Scholar 

  29. Fatema, N., Hua, G., Xiang, Y., Peng, D., & Natgunanathan, I. (2018). Massive MIMO linear precoding: A survey. IEEE Systems Journal, 12(4), 3920–3931.

    Article  Google Scholar 

  30. Parfait, T., Kuang, Y., & Jerry, K. (2014). In Proc. Sixth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 383–388). Shanghai, China.

  31. Müller, A., Couillet, R., Björnson, E., Wagner, S., & Debbah, M. (2015). Interference-aware RZF precoding for multicell downlink systems. IEEE Transactions on Signal Processing, 63(15), 3959–3973.

    Article  MathSciNet  MATH  Google Scholar 

  32. Park, J., & Clerckx, B. (2015). Multi-user linear precoding for multi-polarized massive MIMO system under imperfect CSIT. IEEE Transactions on Wireless Communications, 14(5), 2532–2547.

    Article  Google Scholar 

  33. Müller, A., Kammoun, A., Björnson, E., & Debbah, M. (2016). Linear precoding based on polynomial expansion: Reducing complexity in massive MIMO. EURASIP Journal on Wireless Communications and Networking, 2016, 1–22.

    Google Scholar 

  34. Gaspar, I., Michailow, N., Navarro, A., Ohlmer, E., Krone, S., & Fettweis, G. (2013). In 2013 IEEE 77th vehicular technology conference (VTC Spring). Germany: Dresden.

  35. Michailow, N., Gaspar, I., Krone, S., Lentmaier, M., & Fettweis, G. (2012). In 2012 international symposium on wireless communication systems (ISWCS). Paris: France.

  36. Ehsanfar, S., Matthé, M., Chafii, M., & Fettweis, G. P. (2019). Pilot- and CP-aided channel estimation in MIMO non-orthogonal multi-carriers. IEEE Transactions on Wireless Communications, 18(1), 650–664.

    Article  Google Scholar 

  37. Horn, R.A., & Johnson, C.R. (1991). Topics in matrix analysis. Cambridge University Press

  38. Lei, Z., & Lim, T. (1998). Simplified polynomial-expansion linear detectors for DS-CDMA systems. Electronics Letters, 34(16), 1561–1563.

    Article  Google Scholar 

  39. Kay, S. M. (1993). Fundamentals of Statistical Signal Processing: Estimation Theory. Upper Saddle River, NJ: Prentice Hall.

    MATH  Google Scholar 

  40. Trees, H. L. V. (1968). Detection, Estimation amd Modulation Theory (Vol. 1). New York: Wiley.

    MATH  Google Scholar 

  41. Sessler, G. M. A., & 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 

  42. Zepernick, H., & Finger, A. (2005). Pseudo random signal processing: theory and application (chap. 5, West Sussex PO19 8SQ,) England: Wiley.

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Acknowledgements

The authors would like to thank the China Scholarship Council for its funding support for the work.

Funding

Yanpeng Wang received a scholarship from the China Scholarship Council (Grant No. 201608130110).

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YW and PF: jointly conceived the fundamental idea of this work. YW: developed the methodology, conducted related simulations, and wrote the paper. PF: provided critical guidance on the methodology and simulations and revised the paper.

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Correspondence to Yanpeng Wang.

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Wang, Y., Fortier, P. Polynomial Expansion-Based MMSE Channel Estimation and Precoding for Massive MIMO-GFDM Systems. Wireless Pers Commun 128, 109–129 (2023). https://doi.org/10.1007/s11277-022-09943-0

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