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Complex Regularized Zero Forcing Precoding for Massive MIMO Systems

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Abstract

In this paper, an energy efficient precoding scheme is proposed for massive multiple-input multiple-output (MIMO) systems. The proposed precoding scheme outperforms the conventional zero forcing (ZF) precoder in terms of the power consumption and the achievable transmission rate by including a complex valued regularization parameter such that the energy efficiency is improved. The proposed energy efficient complex regularized zero forcing precoder is analytically evaluated, verified by simulations and compared to the ZF precoder. The obtained results confirm the robustness of the proposed precoder in massive MIMO systems.

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References

  1. Onireti, O., Heliot, F., & Imran, M. A. (2012). On the energy efficiency-spectral efficiency trade-off in the uplink of CoMP system. IEEE Transactions on Wireless Communications, 11(2), 556–561

    Article  Google Scholar 

  2. Sharma, S. K., Patwary, M., & Abdel-Maguid, M. (2013). Spectral efficient compressive transmission framework for wireless communication systems. IET Signal Process, 7(7), 558–564

    Article  MathSciNet  Google Scholar 

  3. Feng, D., Jiang, C., Lim, G., Cimini, L. J., Jr., Feng, G., & Li, G. Y. (2013). A survey of energy-efficient wireless communications. IEEE Communications Surveys and Tutorials, 15(1), 167–178

    Article  Google Scholar 

  4. Mohammed, S. (2014). Impact of transceiver power consumption on the energy efficiency of zero-forcing detector in massive MIMO systems. IEEE Transactions on Communications, 62(11), 3874–3890

    Article  Google Scholar 

  5. Chai, X., Liu, T., Xing, C., Xiao, H., & Zhang, Z. (2016). Throughput improvement in cellular networks via full-duplex based device-to-device communications. IEEE Access, 4, 7645–7657

    Article  Google Scholar 

  6. Müller, A., Kammoun, A., Björnson, E., Debbah, M. (2014). Efficient linear precoding for massive MIMO systems using truncated polynomial expansion. In Proceedings of the IEEE 8th Sensor Array Multichannel Signal Processing Workshop (pp. 273–276).

  7. Chen, X., Wang, X., & Chen, X. (2013). Energy-efficient optimization for wireless information and power transfer in large-scale MIMO systems employing energy beamforming. IEEE Wireless Communications Letters, 2(6), 667–670. https://doi.org/10.1109/WCL.2013.092813.130514

    Article  Google Scholar 

  8. Yang, H., Marzetta, T. (2013). Total energy efficiency of cellular large scale antenna system multiple access mobile networks. In Procedoings of the IEEE Online Conference on Green Communications (OnlineGreenComm).

  9. Jiang, J., Dianati, M., Imran, M. A., Tafazolli, R., & Zhang, S. (2014). Energy-efficiency analysis and optimization for virtual-MIMO systems. IEEE Transactions on Vehicular Technology, 63(5), 2272–2283. https://doi.org/10.1109/TVT.2013.2291435

    Article  Google Scholar 

  10. Björnson, E., Sanguinetti, L., Hoydis, J., & Debbah, M. (2015). Optimal design of energy-efficient multi-user MIMO systems: is massive MIMO the answer? IEEE Transactions on Wireless Communications, 14(6), 3059–3075

    Article  Google Scholar 

  11. Mehana, A., & Nosratinia, A. (2014). Diversity of MIMO linear precoding. IEEE Transactions on Information Theory, 60(2), 1019–1038

    Article  MathSciNet  Google Scholar 

  12. Lu, L., Li, G. Y., Swindlehurst, A. L., Ashikhmin, A., & Zhang, R. (2014). An overview of massive MIMO: Benefits and challenges. IEEE Journal of Selected Topics in Signal Processing, 8(5), 742–758

    Article  Google Scholar 

  13. Bjornson, E., Sanguinetti, L., Hoydis, J., Debbah, M. (2014). Designing multi-user MIMO for energy efficiency: When is massive MIMO the answer? In Proceedings of the IEEE Wireless Communication Network Conference (pp. 242–247)

  14. Marzetta, T. L. (2010). Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Transactions on Wireless Communications, 9(11), 3590–3600

    Article  Google Scholar 

  15. Hoydis, J., ten Brink, S., & Debbah, M. (2013). Massive MIMO in the UL/DL of cellular networks: How many antennas do we need? IEEE Journal on Selected Areas in Communications, 31(2), 160–171

    Article  Google Scholar 

  16. Yang, H., & Marzetta, T. L. (2013). Performance of conjugate and zero-forcing beamforming in large-scale antenna systems. IEEE Journal on Selected Areas in Communications, 31(2), 172–179

    Article  Google Scholar 

  17. Mi, D., Dianati, M., Zhang, L., Muhaidat, S., & Tafazolli, R. (2017). Massive MIMO performance with imperfect channel reciprocity and channel estimation error. IEEE Transactions on Communications, 65(9), 3734–3749

    Article  Google Scholar 

  18. Mostafa, M., Newagy, F., & Hafez, I. (2016). Joint complex regularised zero-forcing equalisation and CFO compensation for MIMO SC-FDMA systems. IET Communications, 10(16), 2245–2251

    Article  Google Scholar 

  19. J. J. S. Sánchez. (2011). Analysis of SC-FDMA and OFDMA performance over fading channels. Ph.d. thesis, Malaga University.

  20. Björnson, E., Hoydis, J., Kountouris, M., & Debbah, M. (2014). Massive MIMO systems with non-ideal hardware: Energy efficiency estimation and capacity limits. IEEE Transactions on Information Theory, 60(11), 7112–7139

    Article  MathSciNet  Google Scholar 

  21. Lim, Y., Chae, C., & Caire, G. (2015). Performance analysis of massive MIMO for cell-boundary users. IEEE Transactions on Wireless Communications, 14(12), 6827–6843

    Article  Google Scholar 

  22. Mostafa, M., Abdelhamed, M. A., Newagy, F., Hafez, I. (2019). MIMO SC-FDMA system performance investigation under realistic channel models. In 2019 36th National Radio Science Conference (NRSC), Port Said, Egypt (pp. 145–152). doi: https://doi.org/10.1109/NRSC.2019.8734656.

  23. Li, A., & Masouros, C. (2015). A constellation scaling approach to vector perturbation for adaptive modulation in MU-MIMO. IEEE Wireless Communications Letters, 4(3), 289–292. https://doi.org/10.1109/LWC.2015.2410271

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Suez Canal University, Ismailia, Egypt

    Mohamed Mostafa

  2. Department of Electronics and Communications, Faculty of Engineering, Ain Shams University, Cairo, Egypt

    Fatma Newagy & Ismail Hafez

Authors
  1. Mohamed Mostafa
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  2. Fatma Newagy
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  3. Ismail Hafez
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Correspondence to Mohamed Mostafa.

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Mostafa, M., Newagy, F. & Hafez, I. Complex Regularized Zero Forcing Precoding for Massive MIMO Systems. Wireless Pers Commun 120, 633–647 (2021). https://doi.org/10.1007/s11277-021-08482-4

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