DOI QR코드

DOI QR Code

Blind adaptive receiver for uplink multiuser massive MIMO systems

  • Shin, Joonwoo (Division of Navigation Science, Korea Maritime and Ocean University) ;
  • Seo, Bangwon (Division of Electrical, Electronics and Control Engineering, The Institute of IT Convergence Technology, Kongju National University)
  • Received : 2018.10.10
  • Accepted : 2019.05.20
  • Published : 2020.02.07

Abstract

Herein, we consider uplink multiuser massive multiple-input multiple-output systems when multiple users transmit information symbols to a base station (BS) by applying simple space-time block coding (STBC). At the BS receiver, two detection filters for each user are used to detect the STBC information symbols. One of these filters is for odd-indexed symbols and the other for even-indexed symbols. Using constrained output variance metric minimization, we first derive a special relation between the closed-form optimal solutions for the two detection filters. Then, using the derived special relation, we propose a new blind adaptive algorithm for implementing the minimum output variance-based optimal filters. In the proposed adaptive algorithm, filter weight vectors are updated only in the region satisfying the special relation. Through a theoretical analysis of the convergence speed and a computer simulation, we demonstrate that the proposed scheme exhibits faster convergence speed and lower steady-state bit error rate than the conventional scheme.

Keywords

References

  1. J.G. Andrews et al., What will 5G be? IEEE J. Sel. Areas Commun. 32 (2014), no. 6, 1065-1082. https://doi.org/10.1109/JSAC.2014.2328098
  2. A. Gupta and R.K. Jha, A survey of 5G network: Architecture and emerging technologies, IEEE Access 3 (2015), 1206-1232. https://doi.org/10.1109/ACCESS.2015.2461602
  3. T.L. Marzetta, Noncooperative cellular wireless with unlimited numbers of base station antennas, IEEE Trans. Wireless Commun. 9 (2010), no. 11, 3590-3600. https://doi.org/10.1109/TWC.2010.092810.091092
  4. F. Rusek et al., Scaling up MIMO: Opportunities and challenges with very large array, IEEE Signal Process. Mag. 30 (2013), no. 1, 40-60. https://doi.org/10.1109/MSP.2011.2178495
  5. Y.-H. Nam et al., Full-dimension MIMO (FD-MIMO) for next generation cellular technology, IEEE Commun. Mag. 51 (2013), no. 6, 172-179. https://doi.org/10.1109/MCOM.2013.6525612
  6. E.G. Larsson et al., Massive MIMO for next generation wireless systems, IEEE Commun. Mag. 52 (2014), no. 2, 186-195. https://doi.org/10.1109/MCOM.2014.6736761
  7. L. Lu et al., An overview of massive MIMO: Benefits and challenges, IEEE J. Select. Topics Signal Process. 8 (2014), no. 5, 742-758. https://doi.org/10.1109/JSTSP.2014.2317671
  8. E. Bjornson, E.G. Larsson, and T.L. Marzetta, Massive MIMO: Ten myths and one critical question, IEEE Commun. Mag. 54 (2016), no. 2, 114-123. https://doi.org/10.1109/MCOM.2016.7402270
  9. D.C. Araujo et al., Massive MIMO: Survey and future research topics, IET Commun. 10 (2016), no. 15, 1938-1946. https://doi.org/10.1049/iet-com.2015.1091
  10. S.M. Alamouti, A simple transmit diversity technique for wireless communications, IEEE J. Sel. Areas Commun. 16 (1998), no. 8, 1451-1458. https://doi.org/10.1109/49.730453
  11. V. Tarokh, H. Jafarkhani, and A.R. Calderbank, Space-time block codes from orthogonal designs, IEEE Trans. Inf. Theory 45 (1999), no. 5, 1456-1467. https://doi.org/10.1109/18.771146
  12. E. Basar and U. Aygolu, Full-rate full-diversity STBCs for three and four transmit t antennas, Electron. Lett. 44 (2008), no. 18, 1076-1077. https://doi.org/10.1049/el:20081676
  13. E. Biglieri, Y. Hong, and E. Viterbo, On fast-decodable space-time block codes, IEEE Trans. Inf. Theory 55 (2009), no. 2, 524-530. https://doi.org/10.1109/TIT.2008.2009817
  14. E. Basar and U. Aygolu, High-rate full-diversity space-time block codes for three and four transmit antennas, IET Commun. 3 (2009), no. 8, 1371-1378. https://doi.org/10.1049/iet-com.2008.0697
  15. N. Varshney and A.K. Jagannatham, MIMO-STBC based multiple relay cooperative communication over time-selective Rayleigh fading links with imperfect channel estimates, IEEE Trans. Vehic. Technol. 66 (2017), no. 7, 6009-6025. https://doi.org/10.1109/TVT.2016.2634924
  16. X. Meng, X.-G. Xia, and X. Gao, Omnidirectional space-time block coding for common information broadcasting in massive MIMO systems, IEEE Trans. Wireless Commun. 17 (2018), no. 3, 1407-1417. https://doi.org/10.1109/TWC.2016.2622259
  17. D. Lee, Symbol error rate analysis of scheduled STBC with power allocation in CR-MIMO systems, IEEE Trans. Veh. Technol. 67 (2018), no. 7, 6218-6228. https://doi.org/10.1109/TVT.2018.2816999
  18. P.B. Rapajic and B.S. Vucetic, Adaptive receiver structures for asynchronous CDMA systems, IEEE J. Select. Areas Commun. 12 (1994), no. 4, 685-697. https://doi.org/10.1109/49.286675
  19. S.L. Miller, An adaptive direct-sequence code-division multiple-access receiver for multiuser interference rejection, IEEE Trans. Commun. 43 (1995), no. 2/3/4, 1746-1755. https://doi.org/10.1109/26.380225
  20. M. Honig, U. Madhow, and S. Verdu, Blind adaptive multiuser detection, IEEE Trans. Inform. Theory 41 (1995), no. 4, 944-960. https://doi.org/10.1109/18.391241
  21. X. Wang and H.V. Poor, Blind equalization and multiuser detection in dispersive CDMA channels, IEEE Trans. Commun. 46 (1998), no. 1, 91-103. https://doi.org/10.1109/26.655407
  22. U. Madhow, Blind adaptive interference suppression for direct-sequence CDMA, Proc. IEEE 86 (1998), no. 10, 2049-2069. https://doi.org/10.1109/5.720252
  23. M. Honig and M.K. Tsatsanis, Adaptive techniques for multiuser CDMA receivers, IEEE Signal Process. Mag. 17 (2000), no. 3, 49-61. https://doi.org/10.1109/79.841725
  24. Z. Xu and M.K. Tsatsanis, Blind adaptive algorithms for minimum variance CDMA receivers, IEEE Trans. Commun. 49 (2001), no. 1, 180-194. https://doi.org/10.1109/26.898261
  25. D. Reynolds, X. Wang, and H.V. Poor, Blind adaptive space-time multiuser detection with multiple transmitter and receiver antennas, IEEE Trans. Signal Process. 50 (2002), no. 6, 1261-1276. https://doi.org/10.1109/TSP.2002.1003052
  26. X. Liu, K.C. The, and E. Gunawan, A blind adaptive MMSE multiuser detector over multipath CDMA channels and its analysis, IEEE Trans. Wireless Commun. 7 (2008), no. 1, 90-97. https://doi.org/10.1109/TWC.2008.05354
  27. A. Elnashar, S. Elnoubi, and H.A. El-Mikati, Performance analysis of blind adaptive MOE multiuser receivers using inverse QRD-RLS algorithm, IEEE Trans. Circuits Syst. 55 (2008), no. 1, 398-411. https://doi.org/10.1109/TCSI.2007.913611
  28. R.C. de Lamare and R. Sampaio-Neto, Blind adaptive MIMO receivers for space-time block-coded DS-CDMA systems in multipath channels using the constant modulus criterion, IEEE Trans. Commun. 58 (2010), no. 1, 21-27. https://doi.org/10.1109/TCOMM.2010.01.070549
  29. R.C. de Lamare, R. Sampaio-Neto, and M. Haardt, Blind adaptive constrained constant-modulus reduced-rank interference suppression algorithms based on interpolation and switched decimation, IEEE Trans. Signal Process. 59 (2011), no. 2, 681-695. https://doi.org/10.1109/TSP.2010.2091274
  30. R.C. de Lamare and P.S.R. Diniz, Blind adaptive interference suppression based on set-membership constrained constant-modulus algorithms with dynamic bounds, IEEE Trans. Signal Process. 61 (2013), no. 5, 1288-1301. https://doi.org/10.1109/TSP.2012.2229995
  31. M.Z.A. Bhotto and I.V. Bajic, Constant modulus blind adaptive beamforming based on unscented Kalman filtering, IEEE Signal Process. 22 (2015), no. 4, 474-478. https://doi.org/10.1109/LSP.2014.2362932
  32. S.S. Khalid and S. Abrar, Blind adaptive algorithm for sparse channel equalisation using projections onto ${\ell}$-ball, Electr. Lett. 51 (2015), no. 18, 1422-1424. https://doi.org/10.1049/el.2015.1103
  33. G. Yang et al., A Kalman filter-based blind adaptive multi-user detection algorithm for underwater acoustic networks, IEEE Sensors J. 16 (2016), no. 11, 4023-4033. https://doi.org/10.1109/JSEN.2015.2464814
  34. S. Haykin, Adaptive filter theory, 3rd ed., Prentice-Hall, Englewood Cliffs, NJ, 1996.
  35. Y. Gu et al., Convergence analysis of a deficient length LMS filter and optimal-length sequence to model exponential decay impulse response, IEEE Signal Process. Lett. 10 (2003), no. 1, 4-7. https://doi.org/10.1109/LSP.2002.806704
  36. A. Hjorungnes and D. Gesbert, Complex-valued matrix differentiation: Techniques and key results, IEEE Trans. Signal Process. 55 (2007), no. 6, 2740-2746. https://doi.org/10.1109/TSP.2007.893762
  37. R.A. Horn and C.R. Johnson, Matrix analysis, ch. 4, Cambridge University Press, Cambridge, UK, 1993.