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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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SINR loss and user selection in massive MU-MISO systems with ZFBF

  • Hu, Mengshi (School of Information and Communication Engineering, Beijing University of Posts and Telecommunications) ;
  • Chang, Yongyu (School of Information and Communication Engineering, Beijing University of Posts and Telecommunications) ;
  • Zeng, Tianyi (School of Information and Communication Engineering, Beijing University of Posts and Telecommunications) ;
  • Wang, Bin (School of Information and Communication Engineering, Beijing University of Posts and Telecommunications)
  • Received : 2018.07.15
  • Accepted : 2019.01.05
  • Published : 2019.10.01
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Abstract

Separating highly correlated users can reduce the loss caused by spatial correlation (SC) in multiuser multiple-input multiple-output (MU-MIMO) systems. However, few accurate analyses of the loss caused by SC have been conducted. In this study, we define signal-to-interference-plus-noise ratio (SINR) loss to characterize it in multiuser multiple-input single-output (MU-MISO) systems, and use coefficient of correlation (CoC) to describe the SC between users. A formula is deduced to show the accurate relation between SINR loss and CoC. Based on this relation, we propose a user selection method that utilizes CoC to minimize the average SINR loss of users in massive MU-MISO systems. Simulation results verify the correctness of the relation and show that the proposed user selection method is very effective at reducing the loss caused by SC in massive MU-MISO systems.

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References

  1. E. Castaneda et al., An overview on resource allocation techniques for multi-user MIMO systems, IEEE Commun. Surv. Tut. 19 (2017), no. 1, 239-284. https://doi.org/10.1109/COMST.2016.2618870
  2. G. Y. Li et al., Multi-cell coordinated scheduling and MIMO in LTE, IEEE Commun. Surv. Tut. 16 (2014), no. 2, 761-775. https://doi.org/10.1109/SURV.2014.022614.00186
  3. A. K. Hassan et al., Performance analysis of beamforming in MU-MIMO systems for rayleigh fading channels, IEEE Access 5 (2017), 3709-3720. https://doi.org/10.1109/ACCESS.2017.2682791
  4. W. L. Shen et al., SIEVE: Scalable user grouping for large MUMIMO systems, in Proc. IEEE Int. Conf. Comput. Commun., Hong Kong, Apr. 2015, pp. 1975-1983.
  5. X. Wu, Z. Ma, and Y. Wang, Joint user grouping and resource allocation for multi-user dual layer beamforming in LTE-A, IEEE Commun. Lett. 19 (2015), no. 10, 1822-1825. https://doi.org/10.1109/LCOMM.2015.2458861
  6. J. Li et al., System design and calibration for wideband channel sounding with multiple frequency bands, IEEE Access 5 (2017), 781-793. https://doi.org/10.1109/ACCESS.2017.2649679
  7. L. N. Tran, M. Bengtsson, and B. Ottersten, Iterative precoder design and user scheduling for block-diagonalized systems, IEEE Trans. Signal Proces. 60 (2012), no. 7, 3726-3739. https://doi.org/10.1109/TSP.2012.2192433
  8. E. Castaneda et al., Metrics for rate maximization in multiuser- MISO systems with zero-forcing beamforming, in Proc. Int. Conf. Telecommun., Lisbon, Portugal, May 2014, pp. 1-5.
  9. M. Wang, T. Samarasinghe, and J. S. Evans, Optimizing user selection schemes in vector broadcast channels, IEEE Trans. Commun. 63 (2015), no. 2, 565-577. https://doi.org/10.1109/TCOMM.2015.2389816
  10. P. Lu and H. C. Yang, Sum-rate analysis of multiuser MIMO system with zero-forcing transmit beamforming, IEEE Trans. Commun. 57 (2009), no. 9, 2585-2589. https://doi.org/10.1109/TCOMM.2009.09.070583
  11. Y. Yu et al., Downlink precoding with correlation-based user grouping for multiuser MISO systems, in Proc. IEEE Global Commun. Conf., Singapore, Dec. 2017, pp. 1-6.
  12. M. Alkhaled, E. Alsusa, and W. Pramudito, Adaptive user grouping algorithm for the downlink massive MIMO systems, in Proc. IEEE Wireless Commun. Netw. Conf., Doha, Qatar, Apr. 2016, pp. 1-6.
  13. V. D. Papoutsis and A. P. Stamouli, Chunk-based resource allocation in multicast MISO-OFDMA with average BER constraint, IEEE Commun. Lett. 17 (2013), no. 2, 317-320. https://doi.org/10.1109/LCOMM.2013.010313.122214
  14. V. D. Papoutsis, I. G. Fraimis, and S. A. Kotsopoulos, User selection and resource allocation algorithm with fairness in MISOOFDMA, IEEE Commun. Lett. 14 (2010), no. 5, 411-413. https://doi.org/10.1109/LCOMM.2010.05.100044
  15. V. D. Papoutsis and A. P. Stamouli, Chunk-based and fairnessbased resource allocation in multicast distributed MISO-OFDMA systems, Int. J. Commun. Syst. 27 (2015), no. 11, 3030-3041. https://doi.org/10.1002/dac.2523
  16. T. F. Maciel and A. Klein, On the performance, complexity, and fairness of suboptimal resource allocation for multiuser MIMO-OFDMA systems, IEEE Trans. Veh. Technol. 59 (2010), no. 1, 406-419. https://doi.org/10.1109/TVT.2009.2029438
  17. M. Hu, Low-complexity user selection method in 3D MU-MIMO systems, Electron. Lett. 54 (2018), no. 20, 1176-1178. https://doi.org/10.1049/el.2018.5352
  18. M. Hu et al., User grouping with adaptive group number for massive MIMO downlink systems, in Proc. Int. Symp. Wirel. Personal Multimedia Commun., Bali, Indonesia, Dec. 2017, pp. 459-464.
  19. A. Bayesteh and A. K. Khandani, On the user selection for MIMO broadcast channels, IEEE Trans. Inform. Theory 54 (2008), no. 3, 1086-1107. https://doi.org/10.1109/TIT.2007.915887
  20. M. Ghosh, A comparison of normalizations for ZF precoded MU-MIMO systems in multipath fading channels, IEEE Wireless Commun. Lett. 2 (2013), no. 5, 515-518. https://doi.org/10.1109/WCL.2013.070113.130302
  21. S. K. Mohammed and E. G. Larsson, Improving the performance of the zero-forcing multiuser MISO downlink precoder through user grouping, IEEE Trans. Wirel. Commun. 15 (2016), no. 2, 811-826. https://doi.org/10.1109/TWC.2015.2478878
  22. C.D. Meyer, Matrix Analysis and Applied Linear Algebra, vol. 71, Siam, Philadelphia, PA, 2000.
  23. 3GPP, Study on 3D channel model for LTE, Tech. Report TR 36.873 V12.6.0., 3GPP, 2017.
  24. T. Zeng et al., CNN based LOS/NLOS identification in 3D massive MIMO systems, IEEE Commun. Lett. 22 (2018), no. 12, 2491-2494. https://doi.org/10.1109/LCOMM.2018.2872522
  25. X. Gao et al., Massive MIMO performance evaluation based on measured propagation data, IEEE Trans. Wireless. Commun. 14 (2015), no. 7, 3899-3911. https://doi.org/10.1109/TWC.2015.2414413