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Low-Complexity Massive MIMO Detectors Under Spatial Correlation and Channel Error Estimates

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Abstract

The performance, complexity and effectiveness of various massive MIMO (M-MIMO) detectors are analyzed operating under highly spatial correlated uniform planar arrays (UPA) channels. In such context, M-MIMO systems present a considerable performance degradation and also, in some cases, an increased complexity. Considering this challenging, but realistic practical scenario, various sub-optimal M-MIMO detection structures are evaluated in terms of complexity and bit error rate (BER) performance trade-off. Specifically, the successive interference cancellation, lattice reduction (LR) and likelihood ascent search (LAS) schemes, as well as a hybrid version combining such structures with conventional linear MIMO detection techniques are comparatively investigated, aiming to improve performance. Hence, to provide a comprehensive analysis, we consider the number of antennas varying in a wide range (from conventional to massive MIMO condition), as well as low and high modulation orders, aiming to verify the potential of each MIMO detection technique according to its performance–complexity trade-off. We have also studied the correlation effect when both transmit and receiver sides are equipped with UPA antenna configurations. The BER performance is verified under different conditions, varying the array configurations, combining the detection techniques, increasing the number of antennas and/or the modulation order, especially aiming a near M-MIMO condition, i.e. up to \(64\times 64\) and \(121\times 121\) antennas has been considered. The aggregated LAS technique has demonstrated good performance in scenarios with high number of antennas, while LR and OSIC operates better in high correlated arrangements.

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References

  1. Dirk, W., Dominik, S., Joakim, J., & Gerald, M. (2011). Lattice reduction. IEEE Signal Processing Magazine, 28(3), 70–91.

    Article  Google Scholar 

  2. Wolniansky, P. W., Foschini, G. J., Golden, G. D., & Valenzuela, R. A. (1998). V-BLAST: An architecture for realizing very high data rates over the rich-scattering wireless channel. pp. 295–300.

  3. Boccardi, F., Heath, R. W., 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., 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. Joakim, J. (2004). Maximum likelihood detection for the linear MIMO channel. Ph.D. Thesis, Royal Institute of Technology.

  6. Barbero, L. G., & Thompson, J. S. (2008). Fixing the complexity of the sphere decoder for MIMO detection. IEEE Transactions on Wireless Communications, 7(6), 2131–2142.

    Article  Google Scholar 

  7. Bohnke, R., Wubben, D., Kuhn, V., & Kammeyer, K. D. (2003). Reduced complexity MMSE detection for BLAST architectures. IEEE Global Telecommunications Conference, 4, 2258–2262.

    Article  Google Scholar 

  8. Wubben, D., Bohnke, R., Kuhn, V., & Kammeyer, K. D. (2003). MMSE extension of V-BLAST based on sorted QR decomposition. In: VTC 2003-Fall—IEEE 58th vehicular technology conference (Vol. 1, pp. 508–512).

  9. Ambrozio, V. R., Carlos, M. J., & Taufik, A. (2014). LR-aided MIMO detectors under correlated and imperfectly estimated channels. Wireless Personal Communications, 77(1), 173–196.

    Article  Google Scholar 

  10. Ma, X., & Zhang, W. (2008). Performance analysis for MIMO systems with lattice-reduction aided linear equalization. IEEE Transactions on Communications, 56(2), 309–318.

    Article  MathSciNet  Google Scholar 

  11. Wubben, D., Bohnke, R., Kuuhn, V., & Kammeyer, K.-D. (2004). Near-maximum-likelihood detection of MIMO systems using MMSE-based lattice reduction. In 2004 IEEE international conference on communications (Vol. 2, pp. 798–802).

  12. Chockalingam, A., & Rajan, B. S. (2014). Large MIMO systems. New York, NY: Cambridge University Press.

    Google Scholar 

  13. Vardhan, K. V., Mohammed, S. K., Chockalingam, A., & Rajan, B. S. (2008). A low-complexity detector for large MIMO systems and multicarrier CDMA systems. IEEE Journal on Selected Areas in Communications, 26(3), 473–485.

    Article  Google Scholar 

  14. Larsson, E. G. (2009). MIMO detection methods: How they work [lecture notes]. IEEE Signal Processing Magazine, 26(3), 91–95.

    Article  Google Scholar 

  15. Bai, L., Choi, J., & Yu, Q. (2014). Low complexity MIMO receivers. Berlin: Springer.

    Book  Google Scholar 

  16. Tadashi, K. R., Fernando, C., & Taufik, A. (2015). Efficient near-optimum detectors for large MIMO systems under correlated channels. Wireless Personal Communications, 83(2), 1287–1311.

    Article  Google Scholar 

  17. Soo, C. Y., Jaekwon, K., Young, Y. W., & Kang Chung, G. (2010). MIMO-OFDM wireless communications with MATLAB. New York: Wiley Publishing.

    Google Scholar 

  18. Andrea, G. (2005). Wireless communications. New York, NY: Cambridge University Press.

    Google Scholar 

  19. Van Zelst, A., & Hammerschmidt, J. S. (2002). A single coefficient spatial correlation model for multiple-input multiple-output (MIMO) radio channels. In Proceedings of URSI general assembly (pp. 17–24).

  20. Balanis, C. A. (2005). Antenna theory: Analysis and design. New York: Wiley-Interscience.

    Google Scholar 

  21. Levin, G., & Loyka, S. (2010). On capacity-maximizing angular densities of multipath in MIMO channels. In 2010 IEEE 72nd vehicular technology conference—Fall (pp. 1–5).

  22. Li, J., Su, X., & Zeng, J., et al. (2013). Codebook design for uniform rectangular arrays of massive antennas. In 2013 IEEE 77th vehicular technology conference (VTC Spring) (pp. 1–5).

  23. Boroujerdi, M. N., Haghighatshoar, S., & Caire, G. (2018). Low-complexity statistically robust precoder/detector computation for massive MIMO systems. IEEE Transactions on Wireless Communications, 17(10), 6516–6530.

    Article  Google Scholar 

  24. Tadashi, K. R., & Taufik, A. (2016). Ordered MMSE–SIC via sorted QR decomposition in ill conditioned large-scale MIMO channels. Telecommunication Systems, 63(2), 335–346.

    Article  Google Scholar 

  25. Lenstra, A. K., Lenstra, H. W., & Lovász, L. (1982). Factoring polynomials with rational coefficients. Mathematische Annalen, 261(4), 515–534.

    Article  MathSciNet  MATH  Google Scholar 

  26. Milford, D., & Sandell, M. (2011). Simplified quantisation in a reduced-lattice MIMO decoder. IEEE Communications Letters, 15(7), 725–727.

    Article  Google Scholar 

  27. Mohammed, S. K., Chockalingam, A., & Rajan, B. S. (2008). A low-complexity near-ML performance achieving algorithm for large MIMO detection. In ISIT 2008, Toronto, Canada, July 6–11 (pp. 2012–2016).

  28. Ferreira Silva, G. M., Marinello Filho, J. C., & Abrao, T. (2018). Sequential likelihood ascent search detector for massive MIMO systems. AEU - International Journal of Electronics and Communications, 96(1), 30–39.

    Article  Google Scholar 

  29. Golub Gene, H., & Van Loan Charles, F. (1996). Matrix computations (3rd ed.). Baltimore: JH Univ. Press.

    MATH  Google Scholar 

  30. Ling, C., & Howgrave-Graham, N. (2007). Effective LLL reduction for lattice decoding. In 2007 IEEE international symposium on information theory (pp. 196–200).

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Acknowledgements

This work was supported in part by the National Council for Scientific and Technological Development (CNPq) of Brazil under Grants 304066/2015-0, and in part by CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil (scholarship), and by the Londrina State University - Paraná State Government (UEL).

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Authors and Affiliations

  1. Department of Electrical Engineering, State University of Londrina (DEEL-UEL), Po.Box: 10.011, Londrina, PR, 86057-970, Brazil

    João Lucas Negrão, Giovanni Maciel Ferreira Silva, José Carlos Marinello Filho & Taufik Abrão

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  1. João Lucas Negrão
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  2. Giovanni Maciel Ferreira Silva
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  4. Taufik Abrão
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Correspondence to Taufik Abrão.

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Negrão, J.L., Ferreira Silva, G.M., Marinello Filho, J.C. et al. Low-Complexity Massive MIMO Detectors Under Spatial Correlation and Channel Error Estimates. Wireless Pers Commun 106, 2335–2358 (2019). https://doi.org/10.1007/s11277-019-06320-2

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