MMSE-based iterative processing with imperfect channel and parity check in MIMO systems

MMSE-based iterative processing with imperfect channel and parity check in MIMO systems

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It is known that the acquisition of the complete channel state information at receivers is difficult in multiple-input multiple-output (MIMO) systems. Channel estimation error is unavoidable in practical applications. Under imperfect channel conditions, the channel estimate is directly applied to the equalisation process in the conventional minimum mean-square error (MMSE)-based turbo equalisation scheme. A few studies treat the channel estimation error as an independent component from the channel estimate and slightly enhanced performance is achieved. Unlike the existing work, the authors derive the MMSE-based iterative processing conditioned on channel estimate. Moreover, they note that in low-density parity check coded systems, the parity-check procedure is also involved. The pass in parity check indicates that the message bitstream is successfully recovered. This information can be utilised to reduce the overall computational complexity by degrading the MIMO size since the unknown parameters are reduced. By extending the analysis in a small-scale MIMO system to a large-scale one, they propose to utilise the normalised transmission power in the development. Numerical results show the proposed schemes outperform the existing schemes in terms of system bit error rate and computational complexity performance.


    1. 1)
      • 1. Jiang, F., Li, C., Meng, C., et al: ‘A new turbo e qualizer conditioned on estimated channel for MIMO MMSE receiver’, IEEE Commun. Lett., 2017, 21, (4), pp. 957960.
    2. 2)
      • 2. Jiang, F., Zhang, Y., Li, C.: ‘A new SQRD-based soft interference cancelation scheme in multi-user MIMO SC-FDMA system’, IEEE Commun. Lett., 2017, 21, (4), pp. 821824.
    3. 3)
      • 3. Miridakis, N., Vergados, D.: ‘A survey on the successive interference cancellation performance for single-antenna and multiple-antenna OFDM systems’, IEEE Commun. Surv. Tutor., 2013, 15, (1), pp. 312335.
    4. 4)
      • 4. Yang, S., Hanzo, L.: ‘Fifty years of MIMO detection: the road to large-scale MIMOs’, IEEE Commun. Surv. Tutor., 2015, 17, (4), pp. 19411988.
    5. 5)
      • 5. Gong, Z., Li, C., Jiang, F.: ‘Pilot decontamination in noncooperative massive MIMO cellular networks based on spatial filtering’, IEEE Trans. Wirel. Commun., 2019, 18, (2), pp. 14191433.
    6. 6)
      • 6. Jiang, F., Li, C.: ‘Soft input soft output MMSE-SQRD based turbo e qualization for MIMO-OFDM systems under imperfect channel estimation’. Proc. IEEE GLOBECOM, San Diego, CA, December 2015, pp. 16.
    7. 7)
      • 7. Li, T., Wang, W., Gao, X.: ‘Turbo equalization for LTE uplink under imperfect channel estimation’. Proc. IEEE Personal, Indoor and Mobile Radio Communications (PIMRC), Tokyo, Japan, September 2009, pp. 330334.
    8. 8)
      • 8. Chen, S., Wang, W., Gao, X.: ‘Sorted QR decomposition based detection for MU-MIMO LTE uplink’. Proc. IEEE VTC 2010 – Spring, Taipei, May 2010, pp. 15.
    9. 9)
      • 9. Han, B., Zheng, Y.: ‘Efficient implementation of an iterative MIMO-OFDM receiver using MMSE interference cancelation’, IET Commun., 2014, 8, (7), pp. 990999.
    10. 10)
      • 10. Tao, J.: ‘Single-carrier frequency-domain turbo e qualization with various soft interference cancellation schemes for MIMO systems’, IEEE Trans. Commun., 2015, 63, (9), pp. 32063217.
    11. 11)
      • 11. Liang, L., Peng, H., Li, G., et al: ‘Vehicular communications: a physical layer perspective’, IEEE Trans. Veh. Technol., 2017, 66, (12), pp. 1064710659.
    12. 12)
      • 12. Shah, C., Tsimenidis, C., Sharif, B., et al: ‘Low-complexity iterative receiver structure for time-varying frequency-selective shallow underwater acoustic channels using BICM-ID: design and experimental results’, IEEE J. Ocean. Eng., 2011, 36, (3), pp. 406421.
    13. 13)
      • 13. Zhong, W., Lu, A., Gao, X.: ‘MMSE SQRD based SISO detection for coded MIMO-OFDM systems’, Sci. China Inf. Sci., 2014, 57, (4), pp. 110.
    14. 14)
      • 14. Marzetta, T.: ‘Noncooperative cellular wireless with unlimited numbers of base station antennas’, IEEE Trans. Wirel. Commun., 2010, 9, (11), pp. 35903600.
    15. 15)
      • 15. Gong, Z., Li, C., Jiang, F.: ‘Channel estimation for sparse massive MIMO channels in low SNR regime’, IEEE Trans. Cogn. Commun. Netw., 2018, 4, (4), pp. 883893.
    16. 16)
      • 16. Bjornson, E., Sanguinetti, L., Hoydis, J., et al: ‘Optimal design of energy-efficient multi-user MIMO systems: is massive MIMO the answer?’, IEEE Trans. Wirel. Commun., 2015, 14, (6), pp. 30593075.
    17. 17)
      • 17. Jiang, F., Li, C., Gong, Z.: ‘A low complexity soft-output data detection scheme based on Jacobi method for massive MIMO uplink transmission’. Proc. IEEE Int. Conf. on Communications (ICC), Paris, France, 2017, pp. 15.
    18. 18)
      • 18. Dai, L., Gao, X., Su, X., et al: ‘Low-complexity soft-output signal detection based on Gauss–Seidel method for uplink multiuser large-scale MIMO systems’, IEEE Trans. Veh. Technol., 2015, 64, (10), pp. 48394845.
    19. 19)
      • 19. Tuchle, M., Singer, A., Koetter, R.: ‘Minimum mean squared error equalization using a priori information’, IEEE Trans. Signal Process., 2002, 50, (3), pp. 673683.
    20. 20)
      • 20. Liang, Y., Cheu, E., Bai, L., et al: ‘On the relationship between MMSE-SIC and BI-GDFE receivers for large multiple-input multiple-output channels’, IEEE Trans. Signal Process., 2008, 56, (8), pp. 36273637.
    21. 21)
      • 21. Jiang, F., Li, C., Gong, Z., et al: ‘An iterative approach for massive MIMO uplink processing under imperfect channel conditions’, IEEE Trans. Veh. Technol., 2019, 68, (4), pp. 36423654.
    22. 22)
      • 22. Zeng, J., Lin, J., Wang, Z.: ‘Low complexity message passing detection algorithm for large-scale MIMO systems’, IEEE Wirel. Commun. Lett., 2018, 7, (5), pp. 708711.
    23. 23)
      • 23. Wu, S., Kuang, L., Ni, Z., et al: ‘Low-complexity iterative detection for large-scale multiuser MIMO-OFDM systems using approximate message passing’, IEEE J. Sel. Top. Signal Process., 2014, 8, (5), pp. 902915.
    24. 24)
      • 24. Fan, S., Xiao, Y., Xiao, L., et al: ‘Improved layered message passing algorithms for large-scale g eneralized spatial modulation systems’, IEEE Wirel. Commun. Lett., 2018, 7, (1), pp. 6669.
    25. 25)
      • 25. Narasimhan, T., Chockalingam, A.: ‘Channel hardening-exploiting message passing (CHEMP) receiver in large-scale MIMO systems’, IEEE J. Sel. Top. Signal Process., 2014, 8, (5), pp. 847860.
    26. 26)
      • 26. Cirkic, M., Larsson, E.: ‘SUMIS: near-optimal soft-in soft-out MIMO detection with low and fixed complexity’, IEEE Trans. Signal Process., 2014, 62, (12), pp. 30843097.
    27. 27)
      • 27. Li, X., Ritcey, J.: ‘Bit-interleaved coded modulation with iterative decoding’, IEEE Commun. Lett., 2002, 1, (6), pp. 169171.
    28. 28)
      • 28. Zhang, J., Zheng, Y.: ‘Frequency-domain turbo e qualization with soft successive interference cancellation for single-carrier MIMO underwater acoustic communications’, IEEE Trans. Wirel. Commun., 2011, 10, (9), pp. 28722882.
    29. 29)
      • 29. Wubben, D., Bohnke, R., Kuhn, V., et al: ‘MMSE extension of V-BLAST based on sorted QR decomposition’. Proc. IEEE VTC 2003 – Fall, Orlando, USA, October 2003, pp. 508512.
    30. 30)
      • 30. Wan, P., McGuire, M., Dong, X.: ‘Near-optimal channel estimation for OFDM in fast-fading channels’, IEEE Trans. Signal Process., 2012, 60, (8), pp. 42364253.
    31. 31)
      • 31. Senol, H., Panayirci, E., Poor, V.: ‘Nondata-aided joint channel estimation and e qualization for OFDM systems in very rapidly varying mobile channels’, IEEE Trans. Veh. Technol., 2011, 60, (8), pp. 37803791.
    32. 32)
      • 32. Wang, C., Au, E., Murch, R., et al: ‘On the performance of the MIMO zero-forcing receiver in the presence of channel estimation error’, IEEE Trans. Wirel. Commun., 2007, 6, (3), pp. 805810.
    33. 33)
      • 33. Wang, J., Wen, O., Li, S.: ‘Soft-output MMSE MIMO detector under imperfect channel estimation’. Proc. IEEE Wireless Communications and Networking Conf. (WCNC), Las Vegas, NV, March 2008, pp. 13341338.
    34. 34)
      • 34. Jiang, F., Li, C., Gong, Z.: ‘Accurate analytical BER performance for ZF receivers under imperfect channel in low-SNR region for large receiving antennas’, IEEE Signal Process. Lett., 2018, 25, (8), pp. 12461250.
    35. 35)
      • 35. Wang, J., Wen, O., Li, S.: ‘Soft-output MMSE MIMO detector under ML channel estimation and channel correlation’, IEEE Signal Process. Lett., 2009, 16, (8), pp. 667670.
    36. 36)
      • 36. Jalloul, L., Alex, S., Mansour, M.: ‘Soft-output MIMO detectors with channel estimation error’, IEEE Signal Process. Lett., 2015, 22, (7), pp. 993997.
    37. 37)
      • 37. Jiang, F., Li, C., Gong, Z., et al: ‘Stair matrix and its applications to massive MIMO uplink data detection’, IEEE Trans. Commun., 2018, 66, (6), pp. 24372455.
    38. 38)
      • 38. Jiang, F., Li, C., Gong, Z.: ‘Low complexity and fast processing algorithms for V2I massive MIMO uplink detection’, IEEE Trans. Veh. Technol., 2018, 67, (6), pp. 50545068.
    39. 39)
      • 39. Krishnamoorthy, A., Menon, D.: ‘Matrix inversion using Cholesky decomposition’. Proc. Signal Processing Algorithms, Architectures, Arrangements, and Applications, Poznan, Poland, September 2013, pp. 7075.

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