Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Spatial Based Pilot Allocation (SBPA) in Crowded Massive MIMO Systems

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In this study, a new approach of random access and pilot allocation in crowded scenarios of massive multi-input multi-output (MIMO) systems is proposed. When the number of users in a cell is more than number of orthogonal pilots, random access to pilots (RAP) is a promising solution to solve the massive access problem of users; though the collision of pilots is a by-product of this method. There are several methods for pilot collision resolution such as strongest user collision resolution (SUCR), but they have one major limitation; i.e. they cannot serve users more than the number of orthogonal pilots simultaneously in coherent transmission mode. In this paper, angle of arrival (AOA) and angular spread (AS) of the received pilots in a compressed sensing approach are examined by applying sparsity of massive MIMO channels in angular domain. That allows to allocate same orthogonal pilot to non-overlapped users in spatial domain for coherent data transmission. By applying this approach in newly proposed protocol (SBPA), the limitation in number of adopted simultaneous users in crowded scenarios of a massive MIMO system would be resolved. Numerical results show improvement in access failures rate compared to SUCR in crowded massive MIMO systems.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 13

Similar content being viewed by others

References

  1. Marzetta, T. L., et al. (2010). Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Transactions on Wireless Communications, 9(11), 3590.

    Article  Google Scholar 

  2. Björnson, E., Larsson, E. G., & Debbah, M. (2015). Massive MIMO for maximal spectral efficiency: How many users and pilots should be allocated? IEEE Transactions on Wireless Communications, 15(2), 1293–1308.

    Article  Google Scholar 

  3. Boccardi, F., Heath, R. W, Jr., Lozano, A., Marzetta, T. L., & Popovski, P. (2014). Five disruptive technology directions for 5g. IEEE Communications Magazine, 52(2), 74–80.

    Article  Google Scholar 

  4. Rusek, F., Persson, D., Lau, B. K., Larsson, E. G., Marzetta, T. L., Edfors, O., et al. (2012). Scaling up MIMO: Opportunities and challenges with very large arrays. IEEE Signal Processing Magazine, 30(1), 40–60.

    Article  Google Scholar 

  5. Flordelis, J., Rusek, F., Tufvesson, F., Larsson, E. G., & Edfors, O. (2018). Massive MIMO performance–TDD versus FDD: What do measurements say? IEEE Transactions on Wireless Communications, 17(4), 2247–2261.

    Article  Google Scholar 

  6. Hasan, M., Hossain, E., & Niyato, D. (2013). Random access for machine-to-machine communication in LTE-advanced networks: Issues and approaches. IEEE Communications Magazine, 51(6), 86–93.

    Article  Google Scholar 

  7. 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 

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

    Article  Google Scholar 

  9. 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 

  10. De Carvalho, E., Bjornson, E., Sorensen, J. H., Popovski, P., & Larsson, E. G. (2017). Random access protocols for massive MIMO. IEEE Communications Magazine, 55(5), 216–222.

    Article  Google Scholar 

  11. Sørensen, J.H., De Carvalho, E., & Popovski, P. (2014). Massive MIMO for crowd scenarios: A solution based on random access. In 2014 IEEE Globecom Workshops (GC Wkshps) (pp. 352–357). IEEE.

  12. Sørensen, J. H., De Carvalho, E., Stefanovic, Č., & Popovski, P. (2018). Coded pilot random access for massive MIMO systems. IEEE Transactions on Wireless Communications, 17(12), 8035–8046.

    Article  Google Scholar 

  13. Björnson, E., De Carvalho, E., Sørensen, J. H., Larsson, E. G., & Popovski, P. (2017). A random access protocol for pilot allocation in crowded massive MIMO systems. IEEE Transactions on Wireless Communications, 16(4), 2220–2234.

    Article  Google Scholar 

  14. Han, H., Li, Y., & Guo, X. (2017). A graph-based random access protocol for crowded massive MIMO systems. IEEE Transactions on Wireless Communications, 16(11), 7348–7361.

    Article  Google Scholar 

  15. Sanguinetti, L., D’Amico, A. A., Morelli, M., & Debbah, M. (2018). Random access in massive MIMO by exploiting timing offsets and excess antennas. IEEE Transactions on Communications, 66(12), 6081–6095.

    Article  Google Scholar 

  16. De Carvalho, E., Björnson, E., Sørensen, J. H., Larsson, E. G., & Popovski, P. (2017). Random pilot and data access in massive MIMO for machine-type communications. IEEE Transactions on Wireless Communications, 16(12), 7703–7717.

    Article  Google Scholar 

  17. Senel, K., & Larsson, E. G. (2018). Grant-free massive MTC-enabled massive MIMO: A compressive sensing approach. IEEE Transactions on Communications, 66(12), 6164–6175.

    Article  Google Scholar 

  18. Jiang, H., Qu, D., Ding, J., & Jiang, T. (2019). Multiple preambles for high success rate of grant-free random access with massive MIMO. IEEE Transactions on Wireless Communications, 18(10), 4779–4789.

    Article  Google Scholar 

  19. Ahsan, F., & Sabharwal, A. (2017). Leveraging massive MIMO spatial degrees of freedom to reduce random access delay. In 2017 51st Asilomar conference on signals, systems, and computers (pp. 2007–2011). IEEE.

  20. 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 

  21. He, Y., & Ren, G. (2020). Spatial filtering based random access for machine-type communications in massive MIMO cellular networks. IEEE Transactions on Vehicular Technology.

  22. Zhang, J., Yuan, X., & Zhang, Y.-J. A. (2017). Blind signal detection in massive MIMO: Exploiting the channel sparsity. IEEE Transactions on Communications, 66(2), 700–712.

    Article  Google Scholar 

  23. Miranda, R. K., da Costa, J. P. C., Guo, B., de Almeida, A. L., Del Galdo, G., & de Sousa, R. T. (2019). Low-complexity and high-accuracy semi-blind joint channel and symbol estimation for massive MIMO-OFDM. Circuits, Systems, and Signal Processing, 38(3), 1114–1136.

    Article  Google Scholar 

  24. Bajwa, W. U., Haupt, J., Sayeed, A. M., & Nowak, R. (2010). Compressed channel sensing: A new approach to estimating sparse multipath channels. Proceedings of the IEEE, 98(6), 1058–1076.

    Article  Google Scholar 

  25. Zhou, Y., Herdin, M., Sayeed, A.M., & Bonek, E. (2007). Experimental study of MIMO channel statistics and capacity via the virtual channel representation. Univ. Wisconsin-Madison, Madison, WI, USA, Tech. Rep. (vol. 5, pp. 10–15).

  26. Sayeed, A. M. (2002). Deconstructing multiantenna fading channels. IEEE Transactions on Communications, 50(10), 2563–2579.

    Google Scholar 

  27. Vuokko, L., Kolmonen, V.-M., Salo, J., & Vainikainen, P. (2007). Measurement of large-scale cluster power characteristics for geometric channel models. IEEE Transactions on Antennas and Propagation, 55(11), 3361–3365.

    Article  Google Scholar 

  28. Czink, N., Yin, X., Ozcelik, H., Herdin, M., Bonek, E., & Fleury, B. H. (2007). Cluster characteristics in a MIMO indoor propagation environment. IEEE Transactions on Wireless Communications, 6(4), 1465–1475.

    Article  Google Scholar 

  29. Foucart, S., & Rauhut, H. (2017). A mathematical introduction to compressive sensing. Bulletin of the American Mathematical Society, 54, 151–165.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran

    Abolghasem Afshar & Vahid Tabataba Vakili

Authors
  1. Abolghasem Afshar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vahid Tabataba Vakili
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abolghasem Afshar.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

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

Afshar, A., Vakili, V.T. Spatial Based Pilot Allocation (SBPA) in Crowded Massive MIMO Systems. Wireless Pers Commun 119, 239–257 (2021). https://doi.org/10.1007/s11277-021-08205-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08205-9

Keywords

Search

Navigation