ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

Hybrid combiner design for downlink massive MIMO systems

  • Seo, Bangwon (Division of Electrical, Electronics and Control Engineering, The Institute of IT Convergence Technology, Kongju National University)
  • Received : 2019.03.12
  • Accepted : 2019.09.16
  • Published : 2020.06.08
Download PDF
⟨ Previous Next ⟩

Abstract

We consider a hybrid combiner design for downlink massive multiple-input multiple-output systems when there is residual inter-user interference and each user is equipped with a limited number of radio frequency (RF) chains (less than the number of receive antennas). We propose a hybrid combiner that minimizes the mean-squared error (MSE) between the information symbols and the ones estimated with a constant amplitude constraint on the RF combiner. In the proposed scheme, an iterative alternating optimization method is utilized. At each iteration, one of the analog RF and digital baseband combining matrices is updated to minimize the MSE by fixing the other matrix without considering the constant amplitude constraint. Then, the other matrix is updated by changing the roles of the two matrices. Each element in the RF combining matrix is obtained from the phase component of the solution matrix of the optimization problem for the RF combining matrix. Simulation results show that the proposed scheme performs better than conventional matrix-decomposition schemes.

Keywords

References

  1. D. Zhang et al., Capacity analysis of NOMA with mmWave massive MIMO systems, IEEE J. Sel. Areas Commun. 35 (2017), no. 7, 1606-1618. https://doi.org/10.1109/JSAC.2017.2699059
  2. Z. Ding et al., NOMA meets finite resolution analog beamforming in massive MIMO and millimeter-wave networks, IEEE Commun. Lett. 21 (2017), no. 8, 1879-1882. https://doi.org/10.1109/LCOMM.2017.2700846
  3. L. N. Ribeiro et al., Energy efficiency of mmWave massive MIMO precoding with low-resolution DACs, IEEE J. Sel. Topics Signal Process. 12 (2018), no. 3, 298-312. https://doi.org/10.1109/JSTSP.2018.2824762
  4. G. Yang et al., Low-latency heterogeneous networks with millimeter-wave communications, IEEE Commun. Mag. 56 (2018), no. 6, 124-129. https://doi.org/10.1109/mcom.2018.1700874
  5. S. M. R. Islam et al., Resource allocation for downlink NOMA systems: Key techniques and open issues, IEEE Wireless Commun. 25 (2018), no. 2, 40-47. https://doi.org/10.1109/mwc.2018.1700099
  6. Y. Huang et al., Signal processing for MIMO-NOMA: Present and future challenges, IEEE Wireless Commun. 25 (2018), no. 2, 32-38. https://doi.org/10.1109/mwc.2018.1700108
  7. G. Gui et al., Deep learning for an effective non-orthogonal multiple access scheme, IEEE Trans. Veh. Technol. 67 (2018), no. 9, 8440-8450. https://doi.org/10.1109/TVT.2018.2848294
  8. H. Huang et al., Rate region analysis in a full-duplex-aided cooperative nonorthogonal multiple-access system, IEEE Access 5 (2017), 17869-17880. https://doi.org/10.1109/ACCESS.2017.2747129
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. J. Zhang et al., 3D MIMO for 5G NR: Several observations from 32 to massive 256 antennas based on channel measurement, IEEE Commun. Mag. 56 (2018), no. 3, 62-70. https://doi.org/10.1109/mcom.2018.1700846
  19. H. Huang et al., Deep learning for super-resolution channel estimation and DOA estimation based massive, MIMO system, IEEE Trans. Veh. Technol. 67 (2018), no. 9, 8549-8560. https://doi.org/10.1109/TVT.2018.2851783
  20. C.-S. Lee and W. H. Chung, Hybrid RF-based precoding for cooperative multiuser massive MIMO systems with limited RF chains, IEEE Trans. Commun. 65 (2014), no. 4, 1575-1589. https://doi.org/10.1109/TCOMM.2016.2646359
  21. A. Garcia-Rodriguez et al., Hybrid analog-digital precoding revisited under realistic RF modeling, IEEE Wireless Commun. Lett. 5 (2016), no. 5, 528-531. https://doi.org/10.1109/LWC.2016.2598777
  22. N. Li et al., Hybrid precoding for mmWave massive MIMO systems with partially connected structure, IEEE Access 5 (2017), 15142-15151. https://doi.org/10.1109/ACCESS.2017.2720163
  23. S. Park et al., Exploiting spatial channel covariance for hybrid precoding in massive MIMO systems, IEEE Trans. Signal Process. 65 (2017), no. 14, 3818-3832. https://doi.org/10.1109/TSP.2017.2701321
  24. T.-Y. Chang and C.-E. Chen, A hybrid Tomlinson-Harashima transceiver design for multiuser mmWave MIMO systems, IEEE Wireless Commun. Lett. 7 (2018), no. 1, 118-121. https://doi.org/10.1109/LWC.2017.2757922
  25. M. Li et al., Joint hybrid precoder and combiner design for multi-stream transmission in mmWave MIMO systems, IET Commun. 11 (2017), no. 17, 2596-2604. https://doi.org/10.1049/iet-com.2017.0263
  26. Y. Feng and S. C. Kim, Hybrid precoding in point-to-point massive multiple-input multiple-output systems based on normalized matrix adaptive method, IET Commun. 11 (2017), no. 12, 1882-1885. https://doi.org/10.1049/iet-com.2017.0071
  27. A. Liu and V. Lau, Phase only RF precoding for massive MIMO systems with limited RF chains, IEEE Trans. Signal Process. 62 (2014), no. 17, 4505-4515. https://doi.org/10.1109/TSP.2014.2337840
  28. W. Ni and X. Dong, Hybrid block diagonalization for massive multiuser MIMO systems, IEEE Trans. Commun. 64 (2016), no. 1, 201-211. https://doi.org/10.1109/TCOMM.2015.2502954
  29. R. Rajashekar and L. Hanzo, Iterative matrix decomposition aided block diagonalization for mm-wave multiuser MIMO systems, IEEE Trans. Wireless Comm. 16 (2017), no. 3, 1372-1384. https://doi.org/10.1109/TWC.2016.2628357
  30. W. Ni, X. Dong, and W.-S. Lu, Near-optimal hybrid processing for massive MIMO systems via matrix decomposition, IEEE Trans. Sig. Process. 65 (2017), no. 15, 3922-3933. https://doi.org/10.1109/TSP.2017.2699643
  31. L. Zhao, D. W. K. Ng, and J. Yuan, Multi-user precoding and channels estimation for hybrid millimeter wave systems, IEEE J. Sel. Areas Commun. 35 (2017), no. 7, 1576-1590. https://doi.org/10.1109/JSAC.2017.2699378
  32. N. Kim, Y. Lee, and H. Park, Performance analysis of MIMO system with linear MMSE receiver, IEEE Trans. Wireless Commun. 7 (2008), no. 11, 4474-4478. https://doi.org/10.1109/T-WC.2008.070785
  33. K. V. Mardia, J. T. Kent, and J. M. Bibby, Multivariate analysis (probability and mathematical statistics), Academic Press, San Diego, 1995.
  34. A. J. Rothman et al., Sparse permutation invariant covariance estimation, Electron. J. Stat. 2 (2008), 494-515. https://doi.org/10.1214/08-EJS176
  35. M. Yuan, High dimensional inverse covariance matrix estimation, J. Mach. Learn. Res. 11 (2010), 2261-2286.