Relaxing pre-selector filter selectivity requirements using cognitive RF front-end control

  • Eyosias Yoseph Imana
  • Taeyoung Yang
  • Jeffrey Reed
Article

Abstract

This paper proposes the use of a cognitive engine to control the local oscillator and sampling frequencies in a flexible receiver RF front-end. The analysis in this paper shows that this cognitive engine has the potential to relax selectivity requirement of the pre-selector filter in receiver RF front-end. The cognitive engine is designed by modeling the RF front-end in channelized spectrum domain. The paper also develops a new spectrum occupancy model to evaluate the performance of the approach. Theoretical analysis and simulations are also carried out using the developed model. The results show that the designed cognitive engine can enable a poorly selective receiver to behave similar to highly selective receiver. Furthermore, this paper analyzes the computational complexity of the designed cognitive engine.

Keywords

Dynamic spectrum access Cognitive RF front-end Spectrum occupancy model Spectrum sharing 

References

  1. 1.
    Volos, H. (2010). Cognitive radio engine design for link adaptation. Ph.D. dissertation at Electrical and Computer Engineering Department, Virginia Polytechnic Institute and State University, Blacksburg.Google Scholar
  2. 2.
    Rondeau, T. (2007). Application of artificial intelligence to wireless communications. Ph.D. dissertation at Electrical and Computer Engineering Department, Virginia Polytechnic Institute and State University, Blacksburg.Google Scholar
  3. 3.
    Newman, T. (2008). Multiple objective fitness functions for cognitive radio adaptation. Ph.D. dissertation at Electrical Engineer and Computer Science, University of Kansas, Kansas.Google Scholar
  4. 4.
    IMEC Smart Systems. (2010, March 6). Scaldio reconfigurable radio transceiver IP. Accessed February 2, 2013, http://www2.imec.be/content/user/File/Brochures/GR2010_Leaflet%20Scaldio.pdf.
  5. 5.
    Cafaro, G., Gradishar, T., Heck, J., Machan, S., Nagaraj, G., Olson, S., et al. (2007). A 100 MHz 2.5 GHz Direct Conversion CMOS Transceiver for SDR Applications. In Radio Frequency Integrated Circuits (RFIC) Symposium, 2007 IEEE.Google Scholar
  6. 6.
    Clancy, T. I. (2006). Dynamic spectrum access in cognitive radio networks. Ph.D. disseration at University of Maryland, College Park.Google Scholar
  7. 7.
    Federal Communications Commission. (2010). Mobile broadband: the benefits of additional spectrum. Washington, DC: FCC.Google Scholar
  8. 8.
    Pawelczak, P., Nolan, K., Doyle, L., Oh, S. W., & Cabric, D. (2011). Cognitive radio: Ten years of experimentation and development. IEEE Communication Magazine, 49(3), 90–100.CrossRefGoogle Scholar
  9. 9.
    Roy, M., & Richter, J. (2006). Tunable ferroelectric filters for software defined tactical radios. In Proceedings 15th IEEE Iiternational symposium on the applications of ferroelectrics (pp. 348–351).Google Scholar
  10. 10.
    Nguyen, M.-T., Yan, W., & Horne, E. (2008). Broadband tunable filters using high Q passive tunable ICs. In IEEE MTT-S international microwave symposium digest (pp. 951–954).Google Scholar
  11. 11.
    Brown, A., & Rebeiz, G. (2000). A varactor-tuned RF filter. IEEE Transactions on Microwave Theory and Techniques, 48(7), 1157–1160.CrossRefGoogle Scholar
  12. 12.
    Malczewski, A., Pillans, B., Morris, F., & Newstrom, R. (2011). A family of MEMS tunable filters for advanced RF applications. In IEEE MTT-S international microwave symposium digest (pp. 1–4).Google Scholar
  13. 13.
    Coon, A. (1991). SAW filters and competitive technologies: A comparative review. In Proceedings of IEEE ultrasonics symposium (vol. 1, pp. 155–160).Google Scholar
  14. 14.
    Zhang, H., & Sánchez-Sinencio, E. (2011). Linearization techniques for CMOS low noise amplifiers: A tutorial. IEEE Transactions on Circuits and Systems I, 58(1), 22–36.CrossRefMathSciNetGoogle Scholar
  15. 15.
    Zhang, H., Fan, X., & Sinencio, E. (2009). A low-power, linearized, ultra-wideband LNA design technique. IEEE Journal of Solid-State Circuits, 44(2), 320–330.CrossRefGoogle Scholar
  16. 16.
    Imana, E. Y., & Reed, J. H. (2013). Design of cognitive RF front-end control. In The proceedings of 2012 software defined radio forum, Washington, DC. Google Scholar
  17. 17.
    Razavi, B. (2011). RF microelectronics. Upper Saddle River: Prentice Hall.Google Scholar
  18. 18.
    Proakis, J., & Manalakis, D. (2007). Digital signal processing: Principles, algorithms, and applications. Delhi: Dorling Kindersley.Google Scholar
  19. 19.
    Hasan, S. (2009). Multi-band/multi-mode radio for public safety applications. Ph.D. dissertation at Virginia Tech Electrical and Engineering Department. Google Scholar
  20. 20.
    Marshall, P. (2009). Cognitive radio as a mechanism to manage front-end linearity and dynamic range. Communications Magazine, IEEE, 7(3), 81–87.CrossRefGoogle Scholar
  21. 21.
    Marcus, M. (2010). Can cognitive radio technology help solve some difficult spectrum management issues by creating “virtual guardbands”. IEEE wireless communications, 18(2), 5.CrossRefGoogle Scholar
  22. 22.
    Imana, E. Y., Yang, T., & Reed, J. H. (2013). RF imperfections tolerant multi-band spectrum sensing. In Preparation to IEEE wireless communications letters.Google Scholar
  23. 23.
    Pozar, M. (2001). Microwave and RF design of wireless systems. New York: Wiley.Google Scholar
  24. 24.
    Couch, L. I. (1992). Digital and analog communication systems. Upper Saddle River: Prentice Hall.Google Scholar
  25. 25.
    Rappaport, T. S. (1996). Wireless communications; principles and practice. Upper Saddle River: Prentice Hall.Google Scholar
  26. 26.
    Zaninetti, L., & Ferraro, M. (2008). On the truncated Pareto distribution with applications. Central European Journal of Physics, 6(1), 1–6.CrossRefGoogle Scholar
  27. 27.
    Ghosh, C., Pagadarai, S., Agrawal, D., & Wyglinski, A. (2010). A framework for statistical wireless spectrum occupancy modeling. IEEE Transactions on Wireless Communications 9(1), 38–44.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Eyosias Yoseph Imana
    • 1
  • Taeyoung Yang
    • 1
  • Jeffrey Reed
    • 1
  1. 1.Bradley Department of Electrical and Computer EngineeringVirginia TechBlacksburgUSA

Personalised recommendations