Skip to main content

Advertisement

Log in

Optimal Transmit Antenna Selection Using Improved GSA in Massive MIMO Technology

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Massive Multiple Input Multiple Output (M-MIMO) systems depend on numerous antennas to transfer numerous data streams simultaneously in Wireless Network Systems. In M-MIMO systems, the optimal Transmit Antennas Selection remains as a major constraint. As the count of antennas is increased, the power or energy consumption also increases. In fact, for attaining higher capacity, more transmit antennas is required, which leads to an increase in power consumption. Hence, for solving these problems in M-MIMO systems, this paper intend to achieve the selection of optimal transmit antennas by considering a multi-objective problem that maximizes both the capacity and relative Energy Efficiency. For attaining this objective, the proposed novel optimization algorithm not only optimizes the number of transmit antennas but also optimizes which antenna has to be selected. Hence, for optimal selection of antennas, improved GSA is used here, based on a velocity vector, and hence the proposed scheme is termed as Modified Velocity vector based GSA (MV-GSA) that determines the number of antennas and how to select the antennas in an optimal way. Moreover, the adopted scheme is compared with conventional algorithms like Genetic Algorithm, Artificial Bee Colony, Particle Swarm Optimization, FireFly and conventional GSA and the results are obtained.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

MIMO:

Multiple Input Multiple Output

WNS:

Wireless Network Systems

M-MIMO:

Massive MIMO

AS:

Antennas Selection

TAS:

Transmit Antennas Selection

EE:

Energy efficiency

GSA:

Gravitational search optimization

MV-GSA:

Modified Velocity vector based GSA

GA:

Genetic Algorithm

ABC:

Artificial Bee Colony

LS-MIMO:

Large scale MIMO

PSO:

Particle Swarm Optimization

PA:

Power amplifier

FF:

FireFly

BS:

Base station

RF:

Radio frequency

SE:

Spectral efficiency

MOO:

Multi-objective optimization

ZF:

Zero-forcing

MC:

Monte Carlo

GS:

Global swapping

RMV:

Rectangular maximum-volume

WS-PSO:

Weighted sum PSO

NBI-PSO:

Normal boundary intersection PSO

MEM:

Minimum EIGEN VALUE MAXIMIZATION

GD-AS:

Greedy-search-based AS

IUI:

Inter user interference

MF:

Matched filtering

IBO:

Input-back-off

CSI:

Channel state information

GI:

Guard interval

References

  1. Gao, Y., Vinck, H., & Kaiser, T. (2018). Massive MIMO antenna selection: Switching architectures, capacity bounds, and optimal antenna selection algorithms. IEEE Transactions on Signal Processing, 66(5), 1346–1360.

    Article  MathSciNet  Google Scholar 

  2. Tang, H., & Nie, Z. (2018). Massive MIMO antenna selection algorithms based on iterative swapping. Electronics Letters, 54(4), 190–192.

    Article  Google Scholar 

  3. Hu, B., Liu, Y., Xie, G., Gao, J., & Yang, Y. (2014). Energy efficiency of massive MIMO wireless communication systems with antenna selection. The Journal of China Universities of Posts and Telecommunications, 21(6), 1–8.

    Article  Google Scholar 

  4. Liu, Z., Du, W., & Sun, D. (2017). Energy and spectral efficiency trade off for massive mimo systems with transmit antenna selection. IEEE Transactions on Vehicular Technology, 66(5), 4453–4457.

    Google Scholar 

  5. Olyaee, M., Eslami, M., & Haghighat, J. (2018). An energy-efficient joint antenna and user selection algorithm for multi-user massive MIMO downlink. IET Communications, 12(3), 255–260.

    Article  Google Scholar 

  6. Amadori, P. V., & Masouros, C. (2016). Interference-driven antenna selection for massive multiuser MIMO. IEEE Transactions on Vehicular Technology, 65(8), 5944–5958.

    Article  Google Scholar 

  7. Tang, H., & Nie, Z. (2018). RMV antenna selection algorithm for massive MIMO. IEEE Signal Processing Letters, 25(2), 239–242.

    Article  Google Scholar 

  8. Park, J., Moon, C., Yeom, I., & Kim, Y. (2017). Cardinality estimation using collective interference for large-scale RFID systems. Journal of Network and Computer Applications, 83, 101–110.

    Article  Google Scholar 

  9. Song, R., Wang, Q., Mao, B., Wang, Z., & Mu, S. (2018). Flexible graphite films with high conductivity for radio-frequency antennas. Carbon, 13, 164–1690.

    Article  Google Scholar 

  10. Xu, G., Liu, A., Jiang, W., Xiang, H., & Luo, W. (2014). Joint user scheduling and antenna selection in distributed massive MIMO systems with limited backhaul capacity. China Communications, 11(5), 17–30.

    Article  Google Scholar 

  11. Zhu, F., Wu, N., & Liang, Q. (2017). Channel estimation for massive MIMO with 2-D nested array deployment. Physical Communication, 25(Part 2), 432–437.

    Article  Google Scholar 

  12. Lee, H., Park, S., & Bahk, S. (2017). An opportunistic scheduling algorithm using aged CSI in massive MIMO systems. Computer Networks, 129(Part 1), 284–296.

    Article  Google Scholar 

  13. Park, S., Lee, H., Chae, C.-B., & Bahk, S. (2017). Massive MIMO operation in partially centralized cloud radio access networks. Computer Networks, 115, 54–64.

    Article  Google Scholar 

  14. Ghadyani, M., & Shahzadi, A. (2018). Compressive sensing power control for interference management in D2D underlaid massive MIMO systems. AEU-International Journal of Electronics and Communications, 90, 79–87.

    Article  Google Scholar 

  15. Lee, H.-H., & Lee, J.-Y. (2017). Optimal beamforming-selection spatial precoding using population-based stochastic optimization for massive wireless MIMO communication systems. Journal of the Franklin Institute, 354(10), 4247–4272.

    Article  MathSciNet  Google Scholar 

  16. Zhang, T., Lin, C., Chen, H., Sun, C., & Wang, X. (2018). MTF measurement and analysis of linear array HgCdTe infrared detectors. Infrared Physics & Technology, 88, 123–127.

    Article  Google Scholar 

  17. Muthu, P. S. B., & Ponnusamy, K. (2017). Design of linear precoder for correlated multiuser MIMO system with imperfect CSI. AEU - International Journal of Electronics and Communications, 74, 55–62.

    Article  Google Scholar 

  18. Hei, Y. Q., Zhang, C., & Shi, G. M. (2018). Trade-off optimization between energy efficiency and spectral efficiency in large scale MIMO systems. Energy, 145, 747–753.

    Article  Google Scholar 

  19. Zhao, H., Liu, Z., & Sun, Y. (2018). Energy efficiency optimization for SWIPT in K-user MIMO interference channels. Physical Communication, 27, 197–202.

    Article  Google Scholar 

  20. Jing, J., Xiaoxue, C., & Yongbin, X. (2016). Energy-efficiency based downlink multi-user hybrid beamforming for millimeter wave massive MIMO system. The Journal of China Universities of Posts and Telecommunications, 23(4), 53–62.

    Article  Google Scholar 

  21. Chinnadurai, S., Selvaprabhu, P., Jiang, X., Hai, H., & Lee, M. H. (2018). Worst-case weighted sum-rate maximization in multicell massive MIMO downlink system for 5G communications. Physical Communication, 27, 116–124.

    Article  Google Scholar 

  22. Ghadyani, M., & Shahzadi, A. (2017). Multiple random access for massive MIMO framework: A unified compressive sensing based approach. Computers & Electrical Engineering, 64, 524–536.

    Article  Google Scholar 

  23. Jing, X., Li, A., & Liu, H. (2017). A low-complexity Lanczos-algorithm-based detector with soft-output for multiuser massive MIMO systems. Digital Signal Processing, 69, 41–49.

    Article  Google Scholar 

  24. Zhou, Z., Zhou, S., Jie, G., & Niu, Z. (2014) Energy-efficient antenna selection and power allocation for large-scale multiple antenna systems with hybrid energy supply. In IEEE Global Communications Conference, GLOBECOM 2014. https://doi.org/10.1109/GLOCOM.2014.7037195.

  25. Li, J., Li, S., Mu, X., Zhang, J. (2015). Energy efficiency of very large multiuser MIMO systems with transmit antenna selection. International Journal of Multimedia and Ubiquitous Engineering, 10(6), 243–252.

    Article  Google Scholar 

  26. 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 

  27. Dahlman, E., Parkvall, S., & Skold, J. (2011). 4G: LTE/LTE-advanced for mobile broadband. Cambridge: Academic Press.

    Google Scholar 

  28. Grewal, G. S., & Singh, B. (2018). Efficiency determination of in-service induction machines using gravitational search optimization. Measurement, 118, 156–163.

    Article  Google Scholar 

  29. Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179, 2232–2248.

    Article  Google Scholar 

  30. Vrionis, T. D., Koutiva, X. I., & Vovos, N. A. (2014). A genetic algorithm-based low voltage ride-through control strategy for grid connected doubly fed induction wind generators. IEEE Transactions on Power Systems, 29(3), 1325–1334.

    Article  Google Scholar 

  31. Gao, K. Z., Suganthan, P. N., Pan, Q. K., Tasgetiren, M. F., & Sadollah, A. (2016). Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion. Knowledge-Based Systems, 109, 1–16.

    Article  Google Scholar 

  32. Zhang, J., & Xia, P. (2017). An improved PSO algorithm for parameter identification of nonlinear dynamic hysteretic models. Journal of Sound and Vibration, 389, 153–167.

    Article  Google Scholar 

  33. Wang, H., Wang, W., Zhou, X., Sun, H., & Cui, Z. (2017). Firefly algorithm with neighborhood attraction. Information Sciences, 382–383, 374–387.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Inumula Veeraraghava Rao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rao, I.V., Malleswara Rao, V. Optimal Transmit Antenna Selection Using Improved GSA in Massive MIMO Technology. Wireless Pers Commun 109, 1217–1235 (2019). https://doi.org/10.1007/s11277-019-06611-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06611-8

Keywords

Navigation