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Cognitive Radio as the Facilitator for Advanced Communications Electronic Warfare Solutions

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Abstract

Throughout the 1990s, Software Defined Radio (SDR) technology was viewed almost exclusively as a solution for interoperability problems between various military standards, waveforms and devices. In the meantime, Cognitive Radio (CR) – a novel communication paradigm which embodies SDR with intelligence and self-reconfigurability properties – has emerged. Intelligence and on-the-fly self-reconfiguration abilities of CRs constitute an important next step in the Communications Electronic Warfare, as they may enable the jamming entities with the capabilities of devising and deploying advanced jamming tactics. Similarly, they may also aid the development of the advanced intelligent self-reconfigurable systems for jamming mitigation. This work outlines the development and implementation of the Spectrum Intelligence algorithm for Radio Frequency (RF) interference mitigation. The developed system is built upon the ideas of obtaining relevant spectrum-related data by using wideband energy detectors, performing narrowband waveform identification, extracting the waveforms’ parameters and properly classifying the waveforms. All relevant spectrum activities are continuously monitored and stored. Coupled with the self-reconfigurability of various transmission-related parameters, Spectrum Intelligence is the facilitator for the advanced interference mitigation strategies. The implementation is done on the Cognitive Radio test bed architecture which consists of two military Software Defined Radio terminals, each interconnected with the computationally powerful System-on-Module.

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Notes

  1. We are intentionally creating a distinction between the Temporal Frequency Maps, and the similar but more advanced concept of Radio Environment Maps [11].

References

  1. Mitola, J. (1992). Software radios – survey, critical evaluation and future directions. In National Telesystems Conference, 1992 (NTC-92) (pp. 13/15–13/23).

  2. Mitola, J., & Maguire, G.Q. Jr (1999). Cognitive radio: making software radios more personal. IEEE Personal Communications, 6(4), 13–18.

    Article  Google Scholar 

  3. Zhao, Q., & Sadler, B. (2007). A survey of dynamic spectrum access. IEEE Signal Processing Magazine, 24(3), 79–89.

    Article  Google Scholar 

  4. Mughal, M.O., Marcenaro, L., & Regazzoni, C.S. (2014). Energy detection in multihop cooperative diversity networks: An analytical study. International Journal of Distributed Sensor Networks. doi:10.1155/2014/453248.

  5. Bartoli, G., Marabissi, D., Fantacci, R., Micciullo, L., Armani, C., & Merlo, R. (2012). Performance evaluation of a spectrum-sensing technique for LDACS and JTIDS coexistence in L-band. In Proceedings of SDR’12 -WinnComm-Europe (pp. 17–23).

  6. Tkachenko, A., Cabric, D., & Brodersen, R. (2007). Cyclostationary feature detector experiments using reconfigurable BEE2. In 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2007. DySPAN 2007 (pp. 216–219).

  7. Kapoor, S., Rao, S., & Singh, G. (2011). Opportunistic spectrum sensing by employing matched filter in cognitive radio network. In 2011 International Conference on Communication Systems and Network Technologies (CSNT) (pp. 580–583).

  8. Poisel, R. (2008). Introduction to Communication Electronic Warfare Systems, 2edn. Norwood: USA.

    Google Scholar 

  9. Delaveau, F., Evesti, A., Suomalainen, J., & Shapira, N. (2013). Active and passive eavesdropper threats within public and private civilian wireless-networks – existing and potential future countermeasures – a brief overview. In Proceedings of SDR’13 -WinnComm-Europe (pp. 11–20).

  10. Dabcevic, K., Betancourt, A., Regazzoni, C., & Marcenaro, L. (2014). A fictitious play-based game-theoretical approach to alleviating jamming attacks for cognitive radios. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 8158–8162).

  11. Yilmaz, H., Tugcu, T., Alagöz, F., & Bayhan, S. (2013). Radio environment map as enabler for practical cognitive radio networks. IEEE Communications Magazine, 51(12), 162–169.

    Article  Google Scholar 

  12. Dabcevic, K., Mughal, M.O., Marcenaro, L., & Regazzoni, C.S. (2014). Spectrum intelligence for interference mitigation for cognitive radio terminals. In 2014 Wireless Innovation Forum European Conference on Communications Technologies and Software Defined Radio (WInnComm-Europe 2014).

  13. Digham, F.F., Alouini, M.S., & Simon, M.K. (2007). On the energy detection of unknown signals over fading channels. IEEE Transactions on Communications, 55(1), 21–25.

    Article  Google Scholar 

  14. Cabric, D., Mishra, S., & Brodersen, R. (2004). Implementation issues in spectrum sensing for cognitive radios. In Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004, ( Vol. 1 pp. 772–776).

  15. Zhang, H. (2004). The optimality of naive bayes. In Proceedings of the Seventeenth Florida Artificial Intelligence Research Society Conference (pp. 562–567).

  16. Hirsch, H. (1992). Statistical signal characterization – new help for real-time processing. In Proceedings of the IEEE 1992 National Aerospace and Electronics Conference, 1992. NAECON 1992, (Vol. 1 pp. 121–127).

  17. SelexES: Swave hh specifications (2013). http://www.selexelsag.com/internet/localization/IPC/media/docs/SWave-Handheld-Radio-v1-2012Selex.pdf.

  18. Dabcevic, K., Marcenaro, L., & Regazzoni, C.S. (2014). Spd-driven smart transmission layer based on a software defined radio test bed architecture. In Proceedings of the 4th International Conference on Pervasive and Embedded Computing and Communication Systems (pp. 219–230).

  19. Donoho, D.L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289–1306.

    Article  MathSciNet  MATH  Google Scholar 

  20. Zheng, F., Li, C., & Tian, Z. (2011). Distributed compressive spectrum sensing in cooperative multihop cognitive networks. IEEE Journal of Selected Topics in Signal Processing, 5(1), 37– 48.

    Article  Google Scholar 

  21. Mughal, M.O., Dabcevic, K., Dura, G., Marcenaro, L., & Regazzoni, C.S. (2014). Experimental study of spectrum estimation and reconstruction based on compressive sampling for cognitive radios. In 2014 Wireless Innovation Forum European Conference on Communications Technologies and Software Defined Radio (WInnComm-Europe 2014).

  22. Chen, S., Donoho, D.L., & Saunders, M.A. (1999). Atomic decomposition by basis pursuit. SIAM. Journal of Scientific Computing, 20(1), 33–61.

    Article  MathSciNet  MATH  Google Scholar 

  23. Tropp, J.A., & Gilbert, A.C. (2007). Signal recovery from random measurements via ofthogonal matching pursuit. IEEE Transactions on Information Theory, 53(12), 4655–4666.

    Article  MathSciNet  MATH  Google Scholar 

  24. Feng, P., & Bresler, Y. (1996). Spectrum-blind minimum-rate sampling and reconstruction of multiband signals. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Vol. 3 pp. 1685–1691).

  25. Yu, Z., Hoyos, S., & Sadler, M. (2008). Mixed-signal parallel compressed sensing and reception for cognitive radio. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (pp. 3861–3864).

  26. Laska, J., Kirolos, S., Massoud, Y., Baraniuk, R., Gilbert, A., Iwen, M., & Strauss, M. (2006). Random sampling for analog-to-information conversion of wideband signals. In Proceedings of the IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software (pp. 119–122).

  27. Duarte, M.F., Wakin, M., & Baraniuk, R. (2005). Fast reconstruction of piecewise smooth signals from random projections. In SPARS.

  28. Dabcevic, K., Betancourt, A., Marcenaro, L., & Regazzoni, C. (2014). Intelligent cognitive radio jamming-a game-theoretical approach. EURASIP Journal on Advances in Signal Processing. doi:10.1186/1687-6180-2014-171.

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Acknowledgments

The authors would like to thank Selex ES and Sistemi Intelligenti Integrati Tecnologie (SIIT) for providing the equipment for the test bed, and the laboratory premises for the test bed assembly. Particular acknowledgments go to Virgilio Esposto of Selex ES and to Gabriele Dura of University of Genoa, for providing expertise and technical assistance.

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Correspondence to Kresimir Dabcevic.

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Dabcevic, K., Mughal, M.O., Marcenaro, L. et al. Cognitive Radio as the Facilitator for Advanced Communications Electronic Warfare Solutions. J Sign Process Syst 83, 29–44 (2016). https://doi.org/10.1007/s11265-015-1050-0

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  • DOI: https://doi.org/10.1007/s11265-015-1050-0

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