Skip to main content
Log in

Green Spectrum Sharing: Genetic Algorithm Based SDR Implementation

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Green spectrum sharing techniques share the spectrum with minimum interference to the primary user with reduced transmit power at secondary. Usually, the green spectrum sharing is achieved by operating the cognitive radio in underlay mode by power control mechanism or MIMO cognitive radio based antenna selection mechanism. But the above approaches will not guarantee the Quality of Service (QoS) requirement of the secondary user. Interference Minimization and meeting the QoS requirement of secondary user is modeled as a multi objectives optimization problem and solved using genetic algorithm (GA) in this paper. MIMO cognitive radio system with the GA based power control, antenna selection, and link adaptation is proposed to share the spectrum with minimum interference to primary receiver and QoS assurance of the secondary user. QoS parameter considered under the works are the secondary user bit error rate, band efficiency, and data rate. The GA optimizes the parameters of antenna selection matrix, transmitter power, modulation type, modulation order, the roll-off factor of pulse shaping filter and symbol rate to achieve target QOS. The earlier convergence of the GA is another issue addressed in this work. The earlier convergence of GA results in a local optimum value of parameters, therefore, this work used the hybrid transform for the fitness of individual chromosome. The proposed work is carried out in real time using software defined radio (SDR) platform 6 GHz Vector Signal Generator 5673 configured as a secondary transmitter, Vector Signal Analyzer 5663 as the secondary receiver and two 2 × 2 MIMO USRP RIO SDR 2943R as a primary transmitter and receiver.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Xu, Y., & Zhao, X. (2014). Robust power control for multiuser underlay cognitive radio networks under QoS constraints and interference temperature constraints. Wireless Personal Communications, 75(4), 2383–2397.

    Article  Google Scholar 

  2. Kuo, Y., Yang, J., & Chen, J. (2013). Efficient swarm intelligent algorithm for power control game in cognitive radio networks. IET Communications, 7(11), 1089–1098.

    Article  Google Scholar 

  3. Hossain, E., Bhargava, V. K., & Fettweis, G. P. (2012). Green radio communication networks. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  4. Molisch, A. F., Win, M. Z., & Winters, J. H. (2003). Reduced-complexity transmit/receive diversity systems. IEEE Transactions on Signal Processing-Special Issue on MIMO Wireless Communications, 51(11), 2729–2738.

    Article  MathSciNet  Google Scholar 

  5. Lu, H.-Y., & Fang, W.-H. (2007). Joint transmit/receive antenna selection in MIMO systems based on the priority-based genetic algorithm. IEEE Antennas and Wireless Propagation Letters, 6(1), 588–591.

    Article  MathSciNet  Google Scholar 

  6. Lain, J.-K. (2011). Joint transmit/receive antenna selection for MIMO systems: A real-valued genetic approach. IEEE Communications Letters, 15(1), 58–60.

    Article  Google Scholar 

  7. Fang, W.-H., Huang, S.-C., & Chen, Y.-T. (2011). Genetic algorithm-assisted joint quantized precoding and transmit antenna selection in multi-user multi-input multi-output systems. IET Communication, 5(9), 1220–1229.

    Article  Google Scholar 

  8. Sharma, N., & Madhukumar, A. S. (2015). Genetic algorithm aided proportional fair resource allocation in multicast OFDM systems. IEEE Transactions on Broadcasting, 61(1), 16–29.

    Article  Google Scholar 

  9. Caramia, M., & Dell’Olmo, P. (2008). Multi-objective management in freight logistics increasing capacity, service level and safety with optimization algorithms. New York: Springer.

    Google Scholar 

  10. Zhao, J.-H., Li, F., & Zhang, X.-X. (2012). Parameter adjustment based on improved genetic algorithm for cognitive radio networks. The Journal of China Universities of Posts and Telecommunications, 19(3), 22–26.

    Article  Google Scholar 

  11. National Instruments Corporation. (2016). USRP RIO software defined radio. Available online on http://www.ni.com/datasheet/pdf/en/ds-538. Accessed 26 Apr 2016.

Download references

Acknowledgments

It is to acknowledge that this work is carried out by utilizing the resources funded under the DST-FIST scheme for Electronics and Communication Engineering department of SRM University, Kattankulathur, and Chennai, India.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to P. Vijayakumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vijayakumar, P., Malarvihi, S. Green Spectrum Sharing: Genetic Algorithm Based SDR Implementation. Wireless Pers Commun 94, 2303–2324 (2017). https://doi.org/10.1007/s11277-016-3427-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-016-3427-1

Keywords