Advertisement

Evaluation of Channel Estimation Algorithms Using Practically Measured Channels in FDD Massive MIMO

  • Nikolay DandanovEmail author
  • Krasimir Tonchev
  • Vladimir Poulkov
  • Pavlina Koleva
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 283)

Abstract

An important problem for massive multiple-input multiple-output (MIMO) systems operating with frequency-division duplexing (FDD) is to accurately estimate the channel response with low pilot signal overhead. Most existing algorithms for efficient channel estimation are based on compressive sensing (CS) and assume sparse structure of the channel vector. Relying on it, they try to minimize estimation error and reduce the number of required pilot signals. Utilizing real-world channel responses, we evaluate the performance of 11 state-of-the-art channel estimation algorithms for FDD massive MIMO systems. Results from simulation experiments with channel measurements for carrier frequency in the 2.4 GHz and 5 GHz bands for three environments and two levels of mobility are presented. Channel structures of theoretical and practically measured channels are compared and it is shown that the latter does not follow a specific sparse structure which leads to a significant increase in estimation errors according to our results. A comprehensive analysis of estimation quality and its dependence on signal-to-noise ratio (SNR) and number of pilot signals is provided. The results demonstrate that some algorithms perform well when applied to practical channels while others do not provide confident results. The effects of pilot matrix choice and angular domain channel representation are also studied and evaluated.

Keywords

Channel estimation Massive Mimo Practical channels Frequency-division duplexing Compressive sensing 

Notes

Acknowledgement

This paper is published with the support of project No BG05M2OP001-2.009-0033 “Promotion of Contemporary Research Through Creation of Scientific and Innovative Environment to Encourage Young Researchers in Technical University - Sofia and The National Railway Infrastructure Company in The Field of Engineering Science and Technology Development” within the Intelligent Growth Science and Education Operational Programme co-funded by the European Structural and Investment Funds of the European Union.

References

  1. 1.
    Argos—Practical Many-Antenna MU-MIMO. http://projectargos.org/. Accessed 20 Mar 2019
  2. 2.
    Björnson, E., Hoydis, J., Sanguinetti, L.: Massive MIMO networks: spectral, energy, and hardware efficiency. Found. Trends® Signal Process. 11(3–4), 154–655 (2017).  https://doi.org/10.1561/2000000093CrossRefGoogle Scholar
  3. 3.
    Busari, S.A., Huq, K.M.S., Mumtaz, S., Dai, L., Rodriguez, J.: Millimeter-wave massive MIMO communication for future wireless systems: a survey. IEEE Commun. Surv. Tutor. 20(2), 836–869 (2018).  https://doi.org/10.1109/COMST.2017.2787460CrossRefGoogle Scholar
  4. 4.
    Candes, E.J., Wakin, M.B.: An introduction to compressive sampling. IEEE Signal Process. Mag. 25(2), 21–30 (2008).  https://doi.org/10.1109/MSP.2007.914731CrossRefGoogle Scholar
  5. 5.
    Chen, L., Liu, A., Yuan, X.: Structured turbo compressed sensing for massive MIMO channel estimation using a Markov prior. IEEE Trans. Veh. Technol. 67(5), 4635–4639 (2018).  https://doi.org/10.1109/TVT.2017.2787708CrossRefGoogle Scholar
  6. 6.
    Duarte, M.F., Eldar, Y.C.: Structured compressed sensing: from theory to applications. IEEE Trans. Signal Process. 59(9), 4053–4085 (2011).  https://doi.org/10.1109/TSP.2011.2161982MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Gao, Z., Dai, L., Wang, Z., Chen, S.: Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO. IEEE Trans. Signal Process. 63(23), 6169–6183 (2015).  https://doi.org/10.1109/TSP.2015.2463260MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Guerra, R.E., Anand, N., Shepard, C., Knightly, E.W.: Opportunistic Channel Estimation for Implicit 802.11af MU-MIMO. pp. 60–68. IEEE (2016).  https://doi.org/10.1109/ITC-28.2016.117
  9. 9.
    Larsson, E.G., Edfors, O., Tufvesson, F., Marzetta, T.L.: Massive MIMO for next generation wireless systems. IEEE Commun. Mag. 52(2), 186–195 (2014).  https://doi.org/10.1109/MCOM.2014.6736761CrossRefGoogle Scholar
  10. 10.
    Liang, J., Liu, Y., Zhang, W., Xu, Y., Gan, X., Wang, X.: Joint compressive sensing in wideband cognitive networks. In: 2010 IEEE Wireless Communication and Networking Conference, pp. 1–5, April 2010.  https://doi.org/10.1109/WCNC.2010.5506392
  11. 11.
    Liu, A., Lau, V., Dai, W.: Joint burst LASSO for sparse channel estimation in multi-user massive MIMO. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–6, May 2016.  https://doi.org/10.1109/ICC.2016.7511075
  12. 12.
    Ma, J., Yuan, X., Ping, L.: Turbo compressed sensing with partial DFT sensing matrix. IEEE Signal Process. Lett. 22(2), 158–161 (2015).  https://doi.org/10.1109/LSP.2014.2351822CrossRefGoogle Scholar
  13. 13.
    Needell, D., Tropp, J.A.: CoSaMP: iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmon. Anal. 26(3), 301–321 (2009).  https://doi.org/10.1016/j.acha.2008.07.002MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Rao, X., Lau, V.K.N.: Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems. IEEE Trans. Signal Process. 62(12), 3261–3271 (2014).  https://doi.org/10.1109/TSP.2014.2324991MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Salo, J., et al.: MATLAB implementation of the 3GPP Spatial Channel Model (3GPP TR 25.996), January 2005. http://www.tkk.fi/Units/Radio/scm/
  16. 16.
    Shepard, C., Ding, J., Guerra, R.E., Zhong, L.: Understanding real many-antenna MU-MIMO channels, pp. 461–467. IEEE, November 2016.  https://doi.org/10.1109/ACSSC.2016.7869082
  17. 17.
    Shepard, C., et al.: Argos: Practical Many-antenna Base Stations. p. 53. ACM Press (2012).  https://doi.org/10.1145/2348543.2348553
  18. 18.
    Shepard, C., Yu, H., Zhong, L.: ArgosV2: A Flexible Many-antenna Research Platform. p. 163. ACM Press (2013).  https://doi.org/10.1145/2500423.2505302
  19. 19.
    Vila, J., Schniter, P.: Expectation-maximization Bernoulli-Gaussian approximate message passing. In: 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), pp. 799–803, November 2011.  https://doi.org/10.1109/ACSSC.2011.6190117
  20. 20.
    Yin, H., Gesbert, D., Filippou, M., Liu, Y.: A coordinated approach to channel estimation in large-scale multiple-antenna systems. IEEE J. Sel. Areas Commun. 31(2), 264–273 (2013).  https://doi.org/10.1109/JSAC.2013.130214CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Nikolay Dandanov
    • 1
    Email author
  • Krasimir Tonchev
    • 1
  • Vladimir Poulkov
    • 1
  • Pavlina Koleva
    • 1
  1. 1.Faculty of TelecommunicationsTechnical University of SofiaSofiaBulgaria

Personalised recommendations