Skip to main content

Advertisement

SpringerLink
Log in

A Partial CSI Estimation Approach for Downlink FDD massive-MIMO System with Different Base Transceiver Station Topologies

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Massive multiple-input multiple-output (massive-MIMO) is a promising technology for next generation wireless communications systems due to its capability to increase the data rate and meet the enormous ongoing data traffic explosion. However, in non-reciprocal channels, such as those encountered in frequency division duplex (FDD) systems, channel state information (CSI) estimation using downlink (DL) training sequence is to date very challenging issue, especially when the channel exhibits a shorter coherence time. In particular, the availability of sufficiently accurate CSI at the base transceiver station (BTS) allows an efficient precoding design in the DL transmission to be achieved, and thus, reliable communication systems can be obtained. In order to achieve the aforementioned objectives, this paper presents a feasible DL training sequence design based on a partial CSI estimation approach for an FDD massive-MIMO system with a shorter coherence time. To this end, a threshold-based approach is proposed for a suitable DL pilot selection by exploring the statistical information of the channel covariance matrix. The mean square error of the proposed design is derived, and the achievable sum rate and bit-error-rate for maximum ratio transmitter and regularized zero forcing precoding is investigated over different BTS topologies with uniform linear array and uniform rectangular array. The results show that a feasible performance in the DL FDD massive-MIMO systems can be achieved even when a large number of antenna elements are deployed by the BTS and a shorter coherence time is considered.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. The subscript (sim) stands for the Monte Carlo simulation while the subscript (an) stands for the analytical form.

References

  1. Zhang, Z., Xiao, Y., Ma, Z., Xiao, M., Ding, Z., Lei, X., et al. (2019). 6G wireless networks: Vision, requirements, architecture, and key technologies. IEEE Vehicular Technology Magazine, 14(3), 28–41.

    Article  Google Scholar 

  2. Yang, P., Xiao, Y., Xiao, M., & Li, S. (2019). 6G wireless communications: Vision and potential techniques. IEEE Network, 33(4), 70–75.

    Article  MathSciNet  Google Scholar 

  3. Giordani, M., Polese, M., Mezzavilla, M., Rangan, S., & Zorzi, M. (2020). Toward 6G networks: Use cases and technologies. IEEE Communications Magazine, 58(3), 55–61.

    Article  Google Scholar 

  4. Dang, S., Amin, O., Shihada, B., & Alouini, M.-S. (2020). What should 6G be? Nature Electronics, 3(1), 20–29.

    Article  Google Scholar 

  5. Lu, Y., & Zheng, X. (2020). 6G: A survey on technologies, scenarios, challenges, and the related issues. Journal of Industrial Information Integration, 19,.

    Article  Google Scholar 

  6. Akyildiz, I.F., Kak, A., & Nie, S. (2020). 6G and beyond: The future of wireless communications systems. IEEE Access, vol. 8, pp. 133 995

  7. Zhang, S., Xiang, C., & Xu, S. (2020). 6G: Connecting everything by 1000 times price reduction. IEEE Open Journal of Vehicular Technology, 1, 107–115.

    Article  Google Scholar 

  8. Gui, G., Liu, M., Tang, F., Kato, N., & Adachi, F. (2020). 6G: Opening new horizons for integration of comfort, security, and intelligence. IEEE Wireless Communications, 27(5), 126–132.

    Article  Google Scholar 

  9. Tariq, F., Khandaker, M. R., Wong, K.-K., Imran, M. A., Bennis, M., & Debbah, M. (2020). A speculative study on 6G. IEEE Wireless Communications, 27(4), 118–125.

    Article  Google Scholar 

  10. Han, C., Jornet, J. M., & Akyildiz, I. (2018).“Ultra-massive MIMO channel modeling for graphene-enabled terahertz-band communications. In 2018 IEEE 87th vehicular technology conference (VTC Spring). IEEE (pp. 1–5).

  11. De Carvalho, E., Ali, A., Amiri, A., Angjelichinoski, M., & Heath, R. W. (2020). Non-stationarities in extra-large-scale massive MIMO. IEEE Wireless Communications, 27(4), 74–80.

    Article  Google Scholar 

  12. Ngo, H. Q., Larsson, E. G., & Marzetta, T. L. (2013). Energy and spectral efficiency of very large multiuser MIMO systems. IEEE Transactions on Communications, 61(4), 1436–1449.

    Article  Google Scholar 

  13. 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 

  14. 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 

  15. Marzetta, T. L. (2015). Massive MIMO: An introduction. Bell Labs Technical Journal, 20, 11–22.

    Article  Google Scholar 

  16. Marzetta, T. L., Larsson, E. G., Yang, H., & Ngo, H. Q. (2016). Fundamentals of Massive MIMO. Cambridge University Press.

  17. Medbo, J., Börner, K., Haneda, K., Hovinen, V., Imai, T., Järvelainen, J., Jämsä, T., Karttunen, A., Kusume, K., & Kyröläinen et al. J. (2014). Channel modelling for the fifth generation mobile communications. In The 8th European conference on antennas and propagation (EuCAP 2014). IEEE (pp. 219–223).

  18. Martínez, À. O., De Carvalho, E., & Nielsen, J. Ø. (2014Towards very large aperture massive MIMO: A measurement based study. In 2014 IEEE Globecom workshops (GC Wkshps). IEEE (pp. 281–286).

  19. Jose, J., Ashikhmin, A., Marzetta, T. L., & Vishwanath, S. (2011). Pilot contamination and precoding in multi-cell TDD systems. IEEE Transactions on Wireless Communications, 10(8), 2640–2651.

    Article  Google Scholar 

  20. Yang, H., & Marzetta, T.L. (2013). Total energy efficiency of cellular large scale antenna system multiple access mobile networks. In 2013 IEEE online conference on green communications (OnlineGreenComm). IEEE (pp. 27–32).

  21. Hoydis, J., Ten Brink, S., & Debbah, M. (2013). Massive MIMO in the UL/DL of cellular networks: How many antennas do we need? IEEE Journal on selected Areas in Communications, 31(2), 160–171.

    Article  Google Scholar 

  22. Müller, R. R., Cottatellucci, L., & Vehkapera, M. (2014). Blind pilot decontamination. IEEE Journal of Selected Topics in Signal Processing, 8(5), 773–786.

    Article  Google Scholar 

  23. Björnson, E., Sanguinetti, L., Hoydis, J., & Debbah, M. (2015). Optimal design of energy-efficient multi-user MIMO systems: Is massive MIMO the answer? IEEE Transactions on Wireless Communications, 14(6), 3059–3075.

    Article  Google Scholar 

  24. Yin, H., Cottatellucci, L., Gesbert, D., Müller, R. R., & He, G. (2016). Robust pilot decontamination based on joint angle and power domain discrimination. IEEE Transactions on Signal Processing, 64(11), 2990–3003.

    Article  MathSciNet  MATH  Google Scholar 

  25. Björnson, E., Hoydis, J., & Sanguinetti, L. (2017). Massive MIMO has unlimited capacity. IEEE Transactions on Wireless Communications, 17(1), 574–590.

    Article  Google Scholar 

  26. Ericsson (2016). Ericsson mobility report: On the pulse of the network society. technical report 2016. [online] available: https://www.ericsson.com/assets/local/mobility-report/documents/2016/ericsson-mobility-report-november-2016.pdf.Ericsson.

  27. Kaltenberger, F., Jiang, H., Guillaud, M., & Knopp, R. (2010). “Relative channel reciprocity calibration in MIMO/TDD systems,” in Future Network & Mobile Summit, Florence, Italy. IEEE, 16-18 June, pp. 1–10.

  28. Bjrnson, E., Hoydis, J., Kountouris, M., & Debbah, M. (2013).“Hardware impairments in large-scale MISO systems: Energy efficiency, estimation, and capacity limits,” in 18th International Conference on Digital Signal Processing (DSP), July July, pp. 1–6.

  29. Mi, D., Dianati, M., Zhang, L., Muhaidat, S., & Tafazolli, R. (2017). Massive MIMO performance with imperfect channel reciprocity and channel estimation error. IEEE Transactions on Communications, 65(9), 3734–3749.

    Article  Google Scholar 

  30. Vieira, J., Rusek, F., Edfors, O., Malkowsky, S., Liu, L., & Tufvesson, F. (2017). Reciprocity calibration for massive MIMO: Proposal, modeling, and validation. IEEE Transactions on Wireless Communications, 16(5), 3042–3056.

    Article  Google Scholar 

  31. Tse, D., & Viswanath, P. (2005). Fundamentals of wireless communication. Cambridge University Press.

  32. Hassibi, B., & Hochwald, B. M. (2003). How much training is needed in multiple-antenna wireless links? IEEE Transactions on Information Theory, 49(4), 951–963.

    Article  MATH  Google Scholar 

  33. 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 

  34. Björnson, E., Larsson, E. G., & Marzetta, T. L. (2016). Massive MIMO: Ten myths and one critical question. IEEE Communications Magazine, 54(2), 114–123.

    Article  Google Scholar 

  35. Alsabah, M., Vehkapera, M., & O’Farrell, T. (2020). Non-iterative downlink training sequence design based on sum rate maximization in FDD massive MIMO systems. IEEE Access, 8, 108731–108747.

    Article  Google Scholar 

  36. Naser, M. A., Alsabah, M., Mahmmod, B. M., Noordin, N. K., Abdulhussain, S. H., & Baker, T. (2020). Downlink training design for FDD massive MIMO systems in the presence of colored noise. Electronics, 9(12), 2155.

    Article  Google Scholar 

  37. Adhikary, A., Nam, J., Ahn, J. Y., & Caire, G. (2013). Joint spatial division and multiplexing: The large-scale array regime. IEEE Transactions on Information Theory, 59(10), 6441–6463.

    Article  MathSciNet  MATH  Google Scholar 

  38. Nam, J., Caire, G., & Ha, J. (2017). On the role of transmit correlation diversity in multiuser MIMO systems. IEEE Transactions on Information Theory, 63(1), 336–354.

    Article  MathSciNet  MATH  Google Scholar 

  39. Rao, X., & Lau, V. K. (2014). Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems. IEEE Transactions on Signal Processing, 62(12), 3261–3271.

    Article  MathSciNet  MATH  Google Scholar 

  40. Gao, Z., Dai, L., Wang, Z., & Chen, S. (2015). Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO. IEEE Transactions on Signal Processing, 63(23), 6169–6183.

    Article  MathSciNet  MATH  Google Scholar 

  41. Gao, Z., Dai, L., Dai, W., Shim, B., & Wang, Z. (2016). Structured compressive sensing-based spatio-temporal joint channel estimation for FDD massive MIMO. IEEE Transactions on Communications, 64(2), 601–617.

    Article  Google Scholar 

  42. Han, Y., Lee, J., & Love, D. J. (2017). Compressed sensing-aided downlink channel training for FDD massive MIMO systems. IEEE Transactions on Communications, 65(7), 2852–2862.

    Article  Google Scholar 

  43. Choi, J., Love, D. J., & Bidigare, P. (2014). Downlink training techniques for FDD massive MIMO systems: Open-loop and closed-loop training with memory. IEEE Journal of Selected Topics in Signal Processing, 8(5), 802–814.

    Article  Google Scholar 

  44. Noh, S., Zoltowski, M. D., Sung, Y., & Love, D. J. (2014). Pilot beam pattern design for channel estimation in massive MIMO systems. IEEE Journal of Selected Topics in Signal Processing, 8(5), 787–801.

    Article  Google Scholar 

  45. So, J., Kim, D., Lee, Y., & Sung, Y. (2015). Pilot signal design for massive MIMO systems: A received signal-to-noise-ratio-based approach. IEEE Signal Processing Letters, 22(5), 549–553.

    Article  Google Scholar 

  46. Xie, H., Gao, F., Zhang, S., & Jin, S. (2016). A unified transmission strategy for TDD/FDD massive MIMO systems with spatial basis expansion model. IEEE Transactions on Vehicular Technology, 66(4), 3170–3184.

    Article  Google Scholar 

  47. Stein, S. (1987). Fading channel issues in system engineering. IEEE Journal on Selected Areas in Communications, 5(2), 68–89.

    Article  Google Scholar 

  48. Shiu, D.-S., Foschini, G. J., Gans, M. J., & Kahn, J. M. (2000). Fading correlation and its effect on the capacity of multielement antenna systems. IEEE Transactions on Communications, 48(3), 502–513.

    Article  Google Scholar 

  49. Molisch, A. F. (2012). Wireless communications. Wiley.

  50. Abdulhasan, M. Q., Salman, M. I., Ng, C. K., Noordin, N. K., Hashim, S. J., & Hashim, F. (2015). An adaptive threshold feedback compression scheme based on channel quality indicator (cqi) in long term evolution (lte) system. Wireless Personal Communications, 82(4), 2323–2349.

    Article  Google Scholar 

  51. Abdulhasan, M.Q., Salman, M.I., Ng, C.K., Noordin, N.K., Hashim, S.J., & Hashim, F.B. (2013). Approximate linear minimum mean square error estimation based on channel quality indicator feedback in LTE systems. In 2013 IEEE 11th Malaysia international conference on communications (MICC). IEEE (pp. 446–451).

  52. Abdulhasan, M.Q., Salman, M.I., Ng, C.K., Noordin, N.K., Hashim, S.J., & Hashim, F.B. (2013). A channel quality indicator (CQI) prediction scheme using feed forward neural network (FF-NN) technique for MU-MIMO LTE system. In 2014 IEEE 2nd international symposium on telecommunication technologies (ISTT). IEEE, 2014 (pp. 17–22).

  53. Abdulhasan, M. Q., Salman, M. I., Ng, C. K., Noordin, N. K., Hashim, S. J., & Hashim, F. (2014). Review of channel quality indicator estimation schemes for multi-user MIMO in 3GPP LTE/LTE-A systems. KSII Transactions on Internet & Information Systems, 8(6), 1848–1868.

    Article  Google Scholar 

  54. Salman, M. I., Abdulhasan, M. Q., Ng, C. K., Noordin, N. K., Ali, B. M., & Sali, A. (2017). A partial feedback reporting scheme for LTE mobile video transmission with QoS provisioning. Computer Networks, 112, 108–121.

    Article  Google Scholar 

  55. Kay, S. (1993). Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice-Hall.

  56. Goldsmith, A. (2005). Wireless communications. Cambridge University Press.

  57. Biguesh, M., & Gershman, A. B. (2006). Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals. IEEE Transactions on Signal Processing, 54(3), 884–893.

    Article  MATH  Google Scholar 

  58. Salman, M. I., Abdulhasan, M. Q., Ng, C. K., Noordin, N. K., Sali, A., & Mohd Ali, B. (2013). Radio resource management for green 3g pp long term evolution cellular networks: Review and trade-offs. IETE Technical Review, 30(3), 257–269.

    Article  Google Scholar 

  59. Petersen, K. B., & Michael, S. P. (2008). The matrix cookbook. University of Denmark, 7(15), 1704–1714.

    Google Scholar 

  60. Chizhik, D., Ling, J., Wolniansky, P. W., Valenzuela, R. A., Costa, N., & Huber, K. (2003). Multiple-input-multiple-output measurements and modeling in manhattan. IEEE Journal on Selected Areas in Communications, 21(3), 321–331.

    Article  Google Scholar 

  61. Yu, K., Bengtsson, M., Ottersten, B., McNamara, D., Karlsson, P., & Beach, M. (2004). Modeling of wide-band MIMO radio channels based on NLoS indoor measurements. IEEE Transactions on Vehicular Technology, 53(3), 655–665.

    Article  Google Scholar 

  62. Wallace, J. W., & Jensen, M. A. (2001). Measured characteristics of the MIMO wireless channel. In IEEE 54th vehicular technology conference. VTC Fall 2001, (Vol. 4, pp. 2038–2042).

  63. Gao, X., Edfors, O., Rusek, F., & Tufvesson, F. (2015). Massive MIMO performance evaluation based on measured propagation data. IEEE Transactions on Wireless Communications, 14(7), 3899–3911.

    Article  Google Scholar 

  64. Jakes, W.C., & Cox, D.C. (1994). Microwave mobile communications. Wiley-IEEE Press

  65. Ngo, H. Q., Ashikhmin, A., Yang, H., Larsson, E. G., & Marzetta, T. L. (2017). Cell-free massive MIMO versus small cells. IEEE Transactions on Wireless Communications, 16(3), 1834–1850.

    Article  Google Scholar 

  66. Pouttu, A., Burkhardt, F., Patachia, C., Mendes, L., Brazil, G.R., Pirttikangas, S., Jou, E., Kuvaja, P., Finland, F.T., Heikkilä et al. M. (2020). 6G white paper on validation and trials for verticals towards 2030s1. preprint

Download references

Author information

Authors and Affiliations

  1. Continuous Education Center, The University of Baghdad, Baghdad, 10001, Iraq

    Marwah Abdulrazzaq Naser, Muntadher Qasim Alsabah & Montadar Abas Taher

Authors
  1. Marwah Abdulrazzaq Naser
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muntadher Qasim Alsabah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Montadar Abas Taher
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marwah Abdulrazzaq Naser.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Naser, M.A., Alsabah, M.Q. & Taher, M.A. A Partial CSI Estimation Approach for Downlink FDD massive-MIMO System with Different Base Transceiver Station Topologies. Wireless Pers Commun 119, 3609–3630 (2021). https://doi.org/10.1007/s11277-021-08423-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08423-1

Keywords

Search

Navigation