Skip to main content
SpringerLink
Log in

Likelihood Ascent Search Detection for Coded Massive MU-MIMO Systems to Mitigate IAI and MUI

  • Published:
Radioelectronics and Communications Systems Aims and scope Submit manuscript

Abstract

The main aim of massive multiuser multiple-input multiple-output (MU-MIMO) system is to improve the throughput and spectral efficiency in 5G wireless networks. The performance of MU-MIMO system is severely influenced by inter-antenna interference (IAI) and multiuser interference (MUI). The IAI occurs due to space limitations at each user terminal (UT) and the MUI is added when one UT is in the vicinity of another UT in the same cellular network. IAI can be mitigated through a precoding scheme such as singular value decomposition (SVD), and MUI is suppressed by an efficient multiuser detection (MUD) schemes. The maximum likelihood (ML) detector has optimal performance; however, it has a highly complex structure and involves the need of a large number of computations especially in massive structures. Thus, the neighborhood search-based algorithm such as likelihood ascent search (LAS) has been found to be a better alternative for mitigation of MUI as it results in near optimal performance with low complexity. Most of the recent papers are aimed at eliminating either MUI or IAI, whereas the proposed work presents joint SVD precoding and LAS MUD to mitigate both IAI and MUI. The proposed scheme can achieve a near-optimal performance with smaller number of matrix computations.

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.

Similar content being viewed by others

References

  1. A. Chockalingam and B. Sundar Rajan, Large MIMO Systems, Vol. 9781107026 (Cambridge University Press, 2011). DOI: https://doi.org/10.1017/CBO9781139208437 .

    Google Scholar 

  2. E. G. Larsson, O. Edfors, F. Tufvesson, and T. L. Marzetta, “Massive MIMO for next generation wireless systems,” IEEE Commun. Mag. 52, No. 2, 186 (2014). DOI: https://doi.org/10.1109/MCOM.2014.6736761 .

    Article  Google Scholar 

  3. L. Lu, G. Y. Li, A. L. Swindlehurst, A. Ashikhmin, and R. Zhang, “An overview of massive MIMO: Benefits and challenges,” IEEE J. Sel. Top. Signal Process. 8, No. 5, 742 (2014). DOI: https://doi.org/10.1109/JSTSP.2014.2317671 .

    Article  Google Scholar 

  4. E. Biglieri, R. Calderbank, A. Constantinides, A. Goldsmith, A. Paulraj, and H. V. Poor, MIMO Wireless Communications, Vol. 9780521873 (Cambridge University Press, 2007). DOI: https://doi.org/10.1017/CBO9780511618420 .

    Book  Google Scholar 

  5. Y. Liu, J. Liu, Q. Wu, Y. Zhang, and M. Jin, “A near-optimal iterative linear precoding with low complexity for massive MIMO systems,” IEEE Commun. Lett. 23, No. 6, 1105 (2019). DOI: https://doi.org/10.1109/LCOMM.2019.2911472 .

    Article  Google Scholar 

  6. C. Tang, Y. Tao, Y. Chen, C. Liu, L. Yuan, and Z. Xing, “Approximate iteration detection and precoding in massive MIMO,” China Commun. 15, No. 5, 183 (2018). DOI: https://doi.org/10.1109/CC.2018.8387997 .

    Article  Google Scholar 

  7. N. Fatema, G. Hua, Y. Xiang, D. Peng, and I. Natgunanathan, “Massive MIMO linear precoding: A survey,” IEEE Syst. J. 12, No. 4, 3920 (2018). DOI: https://doi.org/10.1109/JSYST.2017.2776401 .

    Article  Google Scholar 

  8. V. V. Gudla and V. B. Kumaravelu, “Dynamic spatial modulation for next generation networks,” Phys. Commun. 34, 90 (2019). DOI: https://doi.org/10.1016/j.phycom.2019.03.002 .

    Article  Google Scholar 

  9. N. R. Challa and K. Bagadi, “Design of near-optimal local likelihood search-based detection algorithm for coded large-scale MU-MIMO system,” Int. J. Commun. Syst., e4436 (2020). DOI: https://doi.org/10.1002/dac.4436 .

    Article  Google Scholar 

  10. M. Al-Rawi and M. Al-Rawi, “Performance of massive MIMO uplink system over Nakagami-m fading channel,” Radioelectron. Commun. Syst. 60, No. 1, 13 (2017). DOI: https://doi.org/10.3103/S0735272717010022 .

    Article  Google Scholar 

  11. T. A. Sheikh, J. Bora, and M. A. Hussain, “Sum-rate performance of massive MIMO systems in highly scattering channel with semi-orthogonal and random user selection,” Radioelectron. Commun. Syst. 61, No. 12, 547 (2018). DOI: https://doi.org/10.3103/S0735272718120026 .

    Article  Google Scholar 

  12. A. H. Mehana and A. Nosratinia, “Diversity of MIMO linear precoding,” IEEE Trans. Inf. Theory 60, No. 2, 1019 (2014). DOI: https://doi.org/10.1109/TIT.2013.2289860 .

    Article  MathSciNet  MATH  Google Scholar 

  13. K. P. Bagadi, V. Annepu, and S. Das, “Recent trends in multiuser detection techniques for SDMA-OFDM communication system,” Phys. Commun. 20, 93 (2016). DOI: https://doi.org/10.1016/j.phycom.2016.07.001 .

    Article  Google Scholar 

  14. D. Lee, “Performance analysis of zero-forcing-precoded scheduling system with adaptive modulation for multiuser-multiple input multiple output transmission,” IET Commun. 9, No. 16, 2007 (2015). DOI: https://doi.org/10.1049/iet-com.2015.0201 .

    Article  Google Scholar 

  15. X. He, Q. Guo, J. Tong, J. Xi, and Y. Yu, “Low-complexity approximate iterative LMMSE detection for large-scale MIMO systems,” Digit. Signal Process. A Rev. J. 60, 134 (2017). DOI: https://doi.org/10.1016/j.dsp.2016.09.004 .

    Article  Google Scholar 

  16. A. Liu and V. K. N. Lau, “Two-stage constant-envelope precoding for low-cost massive MIMO systems,” IEEE Trans. Signal Process. 64, No. 2, 485 (2016). DOI: https://doi.org/10.1109/TSP.2015.2486749 .

    Article  MathSciNet  MATH  Google Scholar 

  17. A. Hindy and A. Nosratinia, “Ergodic fading MIMO dirty paper and broadcast channels: Capacity bounds and lattice strategies,” IEEE Trans. Wirel. Commun. 16, No. 8, 5525 (2017). DOI: https://doi.org/10.1109/TWC.2017.2712631 .

    Article  Google Scholar 

  18. I. W. Lai, et al., “Spatial permutation modulation for multiple-input multiple-output (MIMO) systems,” IEEE Access 7, 68206 (2019). DOI: https://doi.org/10.1109/ACCESS.2019.2918710 .

    Article  Google Scholar 

  19. L. Gopal, Y. Rong, and Z. Zang, “Tomlinson-Harashima precoding based transceiver design for MIMO relay systems with channel covariance information,” IEEE Trans. Wirel. Commun. 14, No. 10, 5513 (2015). DOI: https://doi.org/10.1109/TWC.2015.2439279 .

    Article  Google Scholar 

  20. R. Masashi Fukuda and T. Abrao, “Linear, quadratic, and semidefinite programming massive MIMO detectors: Reliability and complexity,” IEEE Access 7, 29506 (2019). DOI: https://doi.org/10.1109/ACCESS.2019.2902521 .

    Article  Google Scholar 

  21. W. A. Shehab and Z. Al-Qudah, “Singular value decomposition: Principles and applications in multiple input multiple output communication system,” Int. J. Comput. Networks Commun. 9, No. 1, 13 (2017). DOI: https://doi.org/10.5121/ijcnc.2017.9102 .

    Article  Google Scholar 

  22. W. Liu, L. L. Yang, and L. Hanzo, “SVD-assisted multiuser transmitter and multiuser detector design for MIMO systems,” IEEE Trans. Veh. Technol. 58, No. 2, 1016 (2009). DOI: https://doi.org/10.1109/TVT.2008.927728 .

    Article  Google Scholar 

  23. A. Elghariani and M. Zoltowski, “Low complexity detection algorithms in large-scale MIMO systems,” IEEE Trans. Wirel. Commun. 15, No. 3, 1689 (2016). DOI: https://doi.org/10.1109/TWC.2015.2495163 .

    Article  Google Scholar 

  24. Y. Li, Q. He, and R. S. Blum, “Limited-complexity receiver design for passive/active MIMO radar detection,” IEEE Trans. Signal Process. 67, No. 12, 3258 (2019). DOI: https://doi.org/10.1109/TSP.2019.2911262 .

    Article  MathSciNet  MATH  Google Scholar 

  25. D. C. Araujo, T. Maksymyuk, A. L. F. de Almeida, T. Maciel, J. C. M. Mota, and M. Jo, “Massive MIMO: Survey and future research topics,” IET Commun. 10, No. 15, 1938 (2016). DOI: https://doi.org/10.1049/iet-com.2015.1091 .

    Article  Google Scholar 

  26. M. Mandloi and V. Bhatia, “Error recovery based low-complexity detection for uplink massive MIMO systems,” IEEE Wirel. Commun. Lett. 6, No. 3, 302 (2017). DOI: https://doi.org/10.1109/LWC.2017.2677905 .

    Article  Google Scholar 

  27. K. V. Vardhan, S. K. Mohammed, A. Chockalingam, and B. S. Rajan, “A low-complexity detector for large MIMO systems and multicarrier CDMA systems,” IEEE J. Sel. Areas Commun. 26, No. 3, 473 (2008). DOI: https://doi.org/10.1109/JSAC.2008.080406 .

    Article  Google Scholar 

  28. P. Li and R. D. Murch, “Multiple output selection-LAS algorithm in large MIMO systems,” IEEE Commun. Lett. 14, No. 5, 399 (2010). DOI: https://doi.org/10.1109/LCOMM.2010.05.100092 .

    Article  Google Scholar 

  29. S. Agarwal, A. K. Sah, and A. K. Chaturvedi, “Likelihood-based tree search for low complexity detection in large MIMO systems,” IEEE Wirel. Commun. Lett. 6, No. 4, 450 (2017). DOI: https://doi.org/10.1109/LWC.2017.2702639 .

    Article  Google Scholar 

  30. A. K. Sah and A. K. Chaturvedi, “An unconstrained likelihood ascent based detection algorithm for large MIMO systems,” IEEE Trans. Wirel. Commun. 16, No. 4, 2262 (2017). DOI: https://doi.org/10.1109/TWC.2017.2661283 .

    Article  Google Scholar 

  31. L. Li, W. Meng, and C. Li, “Semidefinite further relaxation on likelihood ascent search detection algorithm for high-order modulation in massive MIMO system,” IET Commun. 11, No. 6, 801 (2017). DOI: https://doi.org/10.1049/iet-com.2016.1160 .

    Article  Google Scholar 

  32. M. Chaudhary, N. K. Meena, and R. S. Kshetrimayum, “Local search based near optimal low complexity detection for large MIMO System,” in 2016 IEEE Int. Conf. on Advanced Networks and Telecommunications Systems, ANTS 2016 (2017). DOI: https://doi.org/10.1109/ANTS.2016.7947792 .

    Chapter  Google Scholar 

  33. A. K. Sah and A. K. Chaturvedi, “Sequential and global likelihood ascent search-based detection in large MIMO systems,” IEEE Trans. Commun. 66, No. 2, 713 (2018). DOI: https://doi.org/10.1109/TCOMM.2017.2761383 .

    Article  Google Scholar 

  34. D. A. Pokamestov, Y. V. Kryukov, E. V. Rogozhnikov, R. R. Abenov, and A. Y. Demidov, “Concepts of the physical level of the fifth generation communications systems,” Radioelectron. Commun. Syst. 60, No. 7, 285 (2017). DOI: https://doi.org/10.3103/S0735272717070019 .

    Article  Google Scholar 

  35. N. R. Challa and K. Bagadi, “Lattice reduction assisted likelihood ascent search algorithm for multiuser detection in massive MIMO system,” Proc. of INDICON 2018 - 15th IEEE India Council Int. Conf. (2018). DOI: https://doi.org/10.1109/INDICON45594.2018.8987139 .

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. VIT University, Vellore, India

    N. R. Challa & K. Bagadi

Authors
  1. N. R. Challa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Bagadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to N. R. Challa.

Ethics declarations

ADDITIONAL INFORMATION

N. R. Challa and K. Bagadi

The authors declare that they have no conflict of interest.

The initial version of this paper in Russian is published in the journal “Izvestiya Vysshikh Uchebnykh Zavedenii. Radioelektronika,” ISSN 2307-6011 (Online), ISSN 0021-3470 (Print) on the link http://radio.kpi.ua/article/view/S0021347020050015 with DOI: https://doi.org/10.20535/S0021347020050015

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Challa, N.R., Bagadi, K. Likelihood Ascent Search Detection for Coded Massive MU-MIMO Systems to Mitigate IAI and MUI. Radioelectron.Commun.Syst. 63, 223–234 (2020). https://doi.org/10.3103/S0735272720050015

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0735272720050015

Search

Navigation