Skip to main content

Advertisement

Springer Nature Link
Log in

A Low-Complexity MAP–SIC Detector for Massive MIMO Systems

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In this paper, we propose a low-complexity maximum a posteriori detector with successive interference cancellation (MAP–SIC) for massive multiple-input multiple-output (MIMO) systems. The basic idea of the proposed MAP–SIC algorithm is to detect and cancel the signal of each user iteratively in order of approximate a posteriori log-likelihood-ratios (LLRs). To obtain the approximate a posteriori LLRs, the proposed method begins with the output of a matched-filter and estimates the mean and variance of interference-plus-noise term for each undetected symbol using the idea of Gaussian approximation. In addition, we also propose a simple strategy based on a posteriori log-likelihood-ratios (LLRs) to update the mean and variance in the iterative process of proposed algorithm. Since there is no need for a matrix inversion and exponential calculations, the complexity of the proposed detector is reduced significantly compared to that of minimum mean squared error (MMSE) and MMSE–SIC. Simulation results substantiate the performance of the proposed detector in the massive MIMO system.

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

Similar content being viewed by others

Data Availability

Yes.

Code Availability

Yes.

Notes

  1. Although we present the detector assuming BPSK here, the proposed detector is applicable to M-QAM and M-PAM as well. Accordingly, we present simulation results for 4-QAM also in Sect. 3.2.

References

  1. 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 

  2. 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 

  3. 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 

  4. 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 

  5. Xiong, C., Zhang, X., Wu, K., & Yang, D. (2009). A simplified fixed-complexity sphere decoder for V-BLAST systems. IEEE Communications Letters, 13(8), 582–584.

    Article  Google Scholar 

  6. Han, S. S., Cui, T., & Tellambura, C. (2012). Improved K-best sphere detection for uncoded and coded MIMO systems. IEEE Communications Letters, 1(5), 472–475.

    Article  Google Scholar 

  7. Qin, X., Yan, Z., & He, G. (2016). A near-optimal detection scheme based on joint steepest descent and Jacobi method for uplink massive MIMO systems. IEEE Communications Letters, 20(2), 276–279.

    Article  Google Scholar 

  8. Dai, L., Gao, X., Su, X., Han, S., Chih-Lin, I., & Wang, Z. (2015). Low-complexity soft output signal detection based on Gauss-Seidel method for uplink multiuser large-scale MIMO systems. IEEE Transactions on Vehicular Technology, 64(10), 4839–4845.

    Article  Google Scholar 

  9. Minango, J., & De Almeida, C. (2018). Low complexity zero forcing detector based on Newton–Schultz iterative algorithm for massive MIMO systems. IEEE Transactions on Vehicular Technology, 67(12), 11759–11766.

    Article  Google Scholar 

  10. Luo, Z., Liu, S., Zhao, M., & Liu, Y. (2007). A novel fast recursive MMSE-SIC detection algorithm for V-BLAST systems. IEEE Transactions on Wireless Communications, 6(6), 2022–2026.

    Article  Google Scholar 

  11. Srinidhi, N., Datta, T., Chockalingam, A., & Rajan, B. S. (2011). Layered Tabu search algorithm for large-MIMO detection and a lower bound on ML performance. IEEE Transactions on Communications, 59(11), 2955–2963.

    Article  Google Scholar 

  12. Zeng, J., Lin, J., & Wang, Z. (2018). Low complexity message passing detection algorithm for large-scale MIMO systems. IEEE Wireless Communications Letters, 7(5), 708–711.

    Article  Google Scholar 

  13. Mohammadkarimi, M., Mehrabi, M., Ardakani, M., & Jing, Y. (2019). Deep Learning-Based Sphere Decoding. IEEE Transactions on Wireless Communications, 18(9), 4368–4378.

    Article  Google Scholar 

  14. He, H., Wen, C., Jin, S., & Li, G. Y. (2020). Model-driven deep learning for MIMO detection. IEEE Transactions on Signal Processing, 68, 1702–1715.

    Article  MathSciNet  Google Scholar 

  15. Yang, S., Wang, L., Lv, T., & Hanzo, L. (2013). Approximate Bayesian probabilistic-data-association-aided iterative detection for MIMO systems using arbitrary M-ary modulation. IEEE Transactions on Vehicular Technology, 62(3), 1228–1240.

    Article  Google Scholar 

  16. Svac, P., Meyer, F., Riegler, E., & Hlawatsch, F. (2013). Soft-heuristic detectors for large MIMO systems. IEEE Transactions on Signal Processing, 61(18), 4573–4586.

    Article  MathSciNet  Google Scholar 

Download references

Funding

None.

Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, Jilin University of Architecture and Technology, Changchun, 130114, China

    Hongzhi Wang & Qingxue Liu

  2. Agricultural Bank of China Changchun College, Changchun, 130012, China

    Xin Miao

Authors
  1. Hongzhi Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xin Miao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Qingxue Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Qingxue Liu.

Ethics declarations

Conflict of interest

All 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

Wang, H., Miao, X. & Liu, Q. A Low-Complexity MAP–SIC Detector for Massive MIMO Systems. Wireless Pers Commun 119, 865–876 (2021). https://doi.org/10.1007/s11277-021-08242-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08242-4

Keywords