Principal Component Analysis-Based Block Diagonalization Precoding Algorithm for MU-MIMO System

  • S. B. M. Priya
  • P. Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 732)


This paper designs a new paradigm for the performance improvement in block diagonalization (BD)-based precoding algorithms for multiple-user MIMO (MU-MIMO) systems. Even though various linear precoding algorithms have been found, they are complicated in terms of receiver architecture with decoder. In order to simplify the user equipment (UE), it is necessary to design a receiver without decoder. This is consummated using principal component analysis (PCA). The PCA along with QR decomposition and minimum mean squared error (MMSE) channel inversion helps in performance improvement and avoids the decoder at the receiver system. The principal component is calculated using QR decomposition instead of traditional singular value decomposition (SVD) decomposition to reduce the computational complexity. The simulation result shows that PCA-based precoding algorithm in comparison with the existing algorithm achieves comparatively better sum rate, lower bit error rate (BER) using a simplified receiver.


BD Lattice reduction (LR) MMSE MU-MIMO PCA Precoding QR decomposition Regularized block diagonalization (RBD) Singular value decomposition (SVD) 


  1. 1.
    Foschini, G.J., Gans, M.J.: On limits of wireless communications in a fading environment when using multiple antennas. Wireless Pers. Commun. 6, 311–335 (1998)Google Scholar
  2. 2.
    Telatar, I.E.: Capacity of multi-antenna Gaussian channels. Eur. Trans. Telecommun. 10, 585–595 (1999)Google Scholar
  3. 3.
    Paulraj, A., Nabar, R., Gore, D.: Introduction to Space-Time Wireless Communications. Cambridge University Press, Cambridge (2003)Google Scholar
  4. 4.
    Tse, D., Viswanath, P.: Fundamentals of Wireless Communications. Cambridge University Press, Cambridge (2005)Google Scholar
  5. 5.
    Vishwanath, S., Jindal, N., Goldsmith, A.: Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels. IEEE Trans. Inf. Theory 49, 2658–2668 (2003)Google Scholar
  6. 6.
    Viswanath, P., Tse, D.N.C.: Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality. IEEE Trans. Inf. Theory 49, 1912–1921 (2003)Google Scholar
  7. 7.
    Spencer, Q., Peel, C., Swindlehurst, A., Haardt, M.: An introduction to the multi-user MIMO downlink. IEEE Commun. Mag. 42(10), 60–67 (2004)Google Scholar
  8. 8.
    Costa, M.: Writing on dirty paper. IEEE Trans. Inf. Theory 29, 439–441 (1983)Google Scholar
  9. 9.
    Tomlinson, M.: New automatic equalizer employing modulo arithmetic. Electron. Lett. 7(5), 138–139 (1971)Google Scholar
  10. 10.
    Harashima, H., Miyakawa, H.: Matched-transmission technique for channels with intersymbol interference. IEEE Trans. Commun. 20(4), 774–780 (1972)Google Scholar
  11. 11.
    Spencer, Q.H., Swindelhurst, A.L., Haardt, M.: Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels. IEEE Trans. Signal Process. 52, 461–471 (2004)Google Scholar
  12. 12.
    Stankovic, V., Haardt, M.: Generalized design of multiuser MIMO precoding matrices. IEEE Trans. Wireless Commun. 7, 953–961 (2008)Google Scholar
  13. 13.
    Sung, H., Lee, H.R., Lee, I.: Generalized channel inversion methods for multiuser MIMO systems. IEEE Trans. Commun. 57(11), 3489–3499 (2009)Google Scholar
  14. 14.
    Zu, K., de Lamare, R.C., Haardt, M.: Generalized design of low-complexity block diagonalization type precoding algorithms for multiuser MIMO systems. IEEE Trans. Commun. 61(10), 4232–4242 (2013)Google Scholar
  15. 15.
    Opmeer, M.R.: Model order reduction by balanced proper orthogonal decomposition and by rational interpolation. IEEE Trans. Automatic Control 57(2), 472–477 (2012)Google Scholar
  16. 16.
    Shlens, J.: A tutorial on principal component analysis.∼elaw/papers/pca.pdf
  17. 17.
    Golub, G.H., Loan, C.F.V.: Matrix Computations, 3rd ed. The Johns Hopkins University Press, Baltimore (1989)Google Scholar
  18. 18.
    Hassibi, B.: An efficient square-root algorithm for BLAST. In: Proceedings of IEEE International Conference on Acoustic, Speech, Signal Processing (ICASSP), pp. 5–9, Istanbul, Turkey (2000)Google Scholar
  19. 19.
    Wubben, D., Bohnk, R., Kuhn, V., Kammeyer, K.D.: MMSE extension of V-BLAST based on sorted QR decomposition. In: Proceedings of IEEE Vehicular Technology Conference (VTC), Orlando, Florida, USA (2003)Google Scholar
  20. 20.
    Bohnk, R., Wubben, D., Kuhn, V., Kammeyer, K.D.: Reduced complexity MMSE detection for BLAST architectures. In: Proceedings of IEEE Global Communications Conference (GLOBECOM), San Francisco, California, USA (2003)Google Scholar
  21. 21.
    Sharma, A., Paliwal, K.K., Imoto, S., Miyano, S.: Principal component analysis using QR decomposition. Int. J. Mach. Learn. Cybern. 4(6), 679–683 (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.JJ College of Engineering and TechnologyTiruchirappalliIndia
  2. 2.Department of Electronics and Communication EngineeringK S Rangasamy College of TechnologyTiruchengode, NamakkalIndia

Personalised recommendations