Skip to main content

Advertisement

SpringerLink
Log in

Low-complexity interference cancellation algorithms for detection in media-based modulated uplink massive-MIMO systems

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

Media-based modulation (MBM) is a novel modulation technique that can improve the spectral efficiency of the existing wireless systems. In MBM, multiple radio frequency (RF) mirrors are placed near the transmit antenna(s) and are switched ON/OFF to create different channel fade realizations. In such systems, additional information is conveyed through the ON/OFF status of RF mirrors along with conventional modulation symbols. A challenging task at the receiver is to detect the transmitted information symbols and extract the additional information from the channel fade realization used for transmission. In this paper, we consider a massive MIMO (mMIMO) system where each user relies on MBM for transmitting information to the base station, and investigate the problem of symbol detection at the base station. First, we propose a mirror activation pattern (MAP) selection based modified iterative sequential detection algorithm. With the proposed algorithm, the most favorable MAP is selected, followed by the detection of symbol corresponding to the selected MAP. Each solution is subjected to the reliability check before getting the update. Next, we introduce a K favorable MAP search based iterative interference cancellation (KMAP-IIC) algorithm. In particular, a selection rule is introduced in KMAP-IIC for deciding the set of favorable MAPs over which iterative interference cancellation is performed, followed by a greedy update scheme for detecting the MBM symbols corresponding to each user. Simulation results show that the proposed detection algorithms exhibit superior performance-complexity trade-off over the existing detection techniques in MBM-mMIMO systems.

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

References

  1. Sun, Y., et al. (2019). Application of machine learning in wireless networks: Key techniques and open issues. IEEE Communications Surveys and Tutorials, 21(4), 3072–3108.

    Article  Google Scholar 

  2. Chen, M., et al. (2019). Artificial neural networks-based machine learning for wireless networks: A tutorial. IEEE Communications Surveys and Tutorials, 21(4), 3039–3071.

    Article  Google Scholar 

  3. Mandloi, M., et al. (Eds.). (2020). 5G and beyond wireless systems: PHY layer perspective. Springer series in wireless technology. Singapore: Springer. https://doi.org/10.1007/978-981-15-6390-4.

    Book  Google Scholar 

  4. Rusek, F., et al. (2013). Scaling up MIMO: Opportunities and challenges with very large arrays. IEEE Signal Processing Magazine, 30(1), 40–60.

    Article  Google Scholar 

  5. Lu, Lu, et al. (2014). An overview of massive MIMO: Benefits and challenges. IEEE Journal on Selected Topics in Signal Processing, 8(5), 742–748.

    Article  Google Scholar 

  6. Larsson, E. G., et al. (2014). Massive MIMO for next generation wireless systems. IEEE Communications Magazine, 52(2), 186–195.

    Article  Google Scholar 

  7. Yang, S., & Hanzo, L. (2015). Fifty years of MIMO detection: The road to large-scale MIMOs. IEEE Communications Survey and Tutorials, 17(4), 1941–1988.

    Article  Google Scholar 

  8. Basar, E. (2016). Index modulation techniques for 5G wireless networks. IEEE Communications Magazine, 54(7), 168–175.

    Article  Google Scholar 

  9. Basar, E. (2020). Reconfigurable intelligent surface-based index modulation: A new beyond MIMO paradigm for 6G. IEEE Transactions on Communications, 68(5), 3187–3196.

    Article  Google Scholar 

  10. Wen, M., Cheng, X., & Yang, L. (2017). Index modulation for 5G wireless communications. New York: Springer.

    Book  Google Scholar 

  11. Mesleh, R. Y., Haas, H., Sinanovic, S., Ahn, C. W., & Yun, S. (2008). Spatial modulation. IEEE Transactions on Vehicular Technology, 57(4), 2228–2241.

    Article  Google Scholar 

  12. Younis, A., Serafimovski, N., Mesleh, R., & Haas, H. (2011). Generalised spatial modulation. In Proceedings of the 44th asilomar conference on signals, systems, and computers (pp. 1498–1502).

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Khandani, A. K. (2013). Media-based modulation: A new approach to wireless transmission. In 2013 IEEE international symposium on information theory (pp. 3050–3054).

  16. Seifi, E., Atamanesh, M., & Khandani, A. K. (2015). Media-based modulation: A new frontier in wireless communications. arXiv preprint arXiv:1507.07516.

  17. Basar, E. (2019). Media-based modulation for future wireless systems: A tutorial. IEEE Wireless Communications, 26(5), 160–166.

    Article  Google Scholar 

  18. Naresh, Y., & Chockalingam, A. (2019). Performance analysis of full-duplex decode-and-forward relaying with media-based modulation. IEEE Transactions on Vehicular Technology, 68(2), 1510–1524.

    Article  Google Scholar 

  19. Shamasundar, B., & Chockalingam, A. (2018). Media-based modulation for the uplink in massive MIMO systems. IEEE Transactions on Vehicular Technology, 67(9), 8169–8183.

    Article  Google Scholar 

  20. Zhang, L., Zhao, M., & Li, L. (2018). Low-complexity multi-user detection for MBM in uplink large-scale MIMO systems. IEEE Communications Letters, 22(8), 1568–1571.

    Article  Google Scholar 

  21. Mesleh, R. Y., et al. (July 2008). Spatial modulation. IEEE Transactions on Vehicular Technology, 57(4), 2228–2241.

    Article  Google Scholar 

  22. Jeganathan, J., Ghrayed, A., & Szczecinski, L. (2008). Spatial modulation: Optimal detection and performance analysis. IEEE Communications Letters, 12(8), 545–547.

    Article  Google Scholar 

  23. Renzo, M. D., et al. (2014). Spatial modulation for generalized MIMO: Challenges, opportunities, and implementation. Proceedings of the IEEE, 102(1), 56–103.

    Article  Google Scholar 

  24. Younis, A., et. al. (2010). Generalized spatial modulation. In 2010 conference record for the forty fourth asilomar conference on signals, systems and computers.

  25. Wang, J., Jia, S., & Song, J. (2012). Generalized spatial modulation system with multiple active transmit antennas and low complexity detection scheme. IEEE Transactions on Wireless Communications, 11(4), 1605–1615.

    Article  Google Scholar 

  26. Narasimhan, T. L., Raviteja, P., & Chockalingam, A. (2015). Generalized spatial modulation in large-scale multiuser MIMO systems. IEEE Transactions on Wireless Communications, 17(7), 3764–3779.

    Article  Google Scholar 

  27. Naresh, Y., & Chockalingam, A. (2017). On media-based modulation using RF mirrors. IEEE Transactions on Vehicular Technology, 66(6), 4967–4983.

    Article  Google Scholar 

  28. Wu, M., et al. (2014). Large-scale MIMO detection for 3GPP LTE: Algorithms and FPGA implementations. IEEE Journal of Selected Topics in Signal Processing, 8(5), 916–929.

    Article  Google Scholar 

  29. Tang, C., Liu, C., & Yuan, L. (2016). High precision low complexity matrix inversion based on Newton iteration for data detection in the massive MIMO. IEEE Communications Letters, 20(3), 490–493.

    Article  Google Scholar 

  30. Mandloi, M., & Bhatia, V. (2017). Low-complexity near-optimal iterative sequential detection for uplink massive MIMO systems. IEEE Communications Letters, 21(3), 568–571.

    Article  Google Scholar 

  31. Dai, L., et al. (2015). Low complexity soft-output signal detection based on Gauss–Seidel method for uplink multiuser large-scale MIMO. IEEE Transactions on Vehicular Technology, 64(10), 4839–4845.

    Article  Google Scholar 

  32. Naresh, Y., & Chockalingam, A. (2018). Performance analysis of media-based modulation with imperfect channel state information. IEEE Transaction on Vehicular Technology, 67(5), 4192–4207.

    Article  Google Scholar 

  33. Narasimhan, T. L., & Chockalingam, A. (2014). Channel hardening-exploiting message passing (CHEMPP) receiver in large-scale MIMO systems. IEEE Journal of Selected Topics in Signal Processing, 8(5), 847–860.

    Article  Google Scholar 

  34. Rajashekar, R., Hari, K. V. S., & Hanzo, L. (2014). Reduced-complexity ML detection and capacity-optimized training for spatial modulation systems. IEEE Transactions on Communications, 62(1), 112–125.

    Article  Google Scholar 

  35. Van, G. H., & Van Loan, C. F. (2012). Matrix computations (Vol. 3). Baltimore: JHU Press.

    Google Scholar 

  36. Bjorck, A. (1996). Numerical methods for least square problems. Philadelphia: SIAM.

    Book  Google Scholar 

Download references

Acknowledgements

This work is supported by the Start-up Research Grant (file no. SRG/2019/000654) scheme of Science and Engineering Research Board, Department of Science and Technology, Government of India.

Author information

Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, SVKM’s NMIMS (Deemed to be University) Shirpur Campus, Shirpur, Maharashtra, 425405, India

    Manish Mandloi

  2. Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, 788010, India

    Devendra Singh Gurjar

Authors
  1. Manish Mandloi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Devendra Singh Gurjar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manish Mandloi.

Ethics declarations

Conflict 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

Mandloi, M., Gurjar, D.S. Low-complexity interference cancellation algorithms for detection in media-based modulated uplink massive-MIMO systems. Telecommun Syst 77, 129–142 (2021). https://doi.org/10.1007/s11235-020-00745-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-020-00745-y

Keywords

Search

Navigation