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Cognitive radios real-time implementation on software defined radio for public safety communications

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Abstract

Cognitive radio (CR) is introduced to efficiently and opportunistically use the limited spectrum to minimize the spectrum scarcity issues. Usually, service providers purchase the spectrum license from the policy-makers and spectrum regulator authority. The non-licensed users cannot utilize the licensed bands even though primary user is not transmitting. It sometimes results in under-utilization of frequency bands, which in turn decreases the spectral efficiency. The CR can opportunistically use the freely available licensed spectrum. To solve the spectrum scarcity problem, we implemented CR using software defined radio on frequency modulated radio band (88–108MHz), because of ease in access and availability. The proposed solution is equally valid for other frequency bands as well, because the printed circuit board adopted here supports frequency from 50MHz to 2.2GHz. The proposed solution uses energy-detection based spectrum-sensing technique for primary user (PU) detection, and afterwards secondary user starts the data transmission over the vacant frequency band. We presented real-time experimental results for the detection probabilities by testing under various false alarm probabilities to verify the efficiency of the designed system. Experimental results show that we can achieve high detection probability even at low receiver sensitivity. When PU becomes active, then the proposed system quickly shift the public-safety user to other available white spaces to avoid halting the licensed user transmission. This solution is beneficial for security and defense organization such as army, traffic wardens, city wardens, and police.

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References

  1. Agarwala, S., Rajagopal, A., Hill, A., Joshi, M., Mullinnix, S., Anderson, T., Damodaran, R., Nardini, L., Wiley, P., Groves, P., et al. (2007). A 65nm c64x+ multi-core dsp platform for communications infrastructure. In 2007 IEEE International solid-state circuits conference. Digest of technical papers (pp. 262–601). IEEE

  2. Akhunzada, A., Ahmed, E., Gani, A., Khan, M. K., Imran, M., & Guizani, S. (2015). Securing software defined networks: taxonomy, requirements, and open issues. IEEE Communications Magazine, 53(4), 36–44.

    Article  Google Scholar 

  3. Akyildiz, I. F., Lee, W. Y., & Chowdhury, K. R. (2009). Crahns: Cognitive radio ad hoc networks. Ad Hoc Networks, 7(5), 810–836.

    Article  Google Scholar 

  4. Akyildiz, I. F., Lee, W. Y., Vuran, M. C., & Mohanty, S. (2006). Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks, 50(13), 2127–2159.

    Article  Google Scholar 

  5. Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C., et al. (2014). What will 5G be? IEEE Journal on Selected Areas in Communications, 32(6), 1065–1082.

    Article  Google Scholar 

  6. Anwar, M. Z., Kaleem, Z., & Jamalipour, A. (2019). Machine learning inspired sound-based amateur drone detection for public safety applications. IEEE Transactions on Vehicular Technology, 68(3), 2526–2534.

    Article  Google Scholar 

  7. Bostian, C. W. (2013). The promise of cognitive radio for communications and remote sensing for critical infrastructure, disaster, safety, and risk management. In US National committee of URSI national radio science meeting (USNC-URSI NRSM) (pp. 1–1). IEEE

  8. Cabric, D., Mishra, S. M., & Brodersen, R. W. (2004). Implementation issues in spectrum sensing for cognitive radios. In Thirty-eighth asilomar conference on signals, systems and computers (Vol. 1, pp. 772–776). IEEE

  9. Clermidy, F., Lemaire, R., Popon, X., Ktenas, D., & Thonnart, Y. (2009). An open and reconfigurable platform for 4g telecommunication: Concepts and application. In 2009 12th Euromicro conference on digital system design, architectures, methods and tools (pp. 449–456). IEEE

  10. Commission, F. C., et al. (2002). Spectrum policy task force report. et docket 02-135.

  11. Delfino, A., Goratti, L., Giuliani, R., Oliveri, F., & Baldini, G. (2013). A software radio implementation of centralized mac protocol for cognitive radio networks. Wireless Personal Communications, 68(3), 1147–1175.

    Article  Google Scholar 

  12. El Bahi, F., Ghennioui, H., & Zouak, M. (2019). Real-time spectrum sensing of multiple OFDM signals using low cost sdr based prototype for cognitive radio. In 2019 15th International wireless communications and mobile computing conference (IWCMC) (pp. 2074–2079). IEEE

  13. Jalier, C., Lattard, D., Jerraya, A.A., Sassatelli, G., Benoit, P., & Torres, L. (2010). Heterogeneous vs homogeneous MPSoC approaches for a mobile LTE modem. In 2010 Design, automation and test in Europe conference and exhibition (DATE 2010) (pp. 184–189). IEEE

  14. Kaleem, Z., Ahmad, A., & Rehmani, M. H. (2018). Neighbors’ interference situation-aware power control scheme for dense 5G mobile communication system. Telecommunication System, 67(3), 443–450.

    Article  Google Scholar 

  15. Kaleem, Z., & Chang, K. (2016). Public safety priority-based user association for load balancing and interference reduction in PS-LTE systems. IEEE Access, 4, 9775–9785.

    Article  Google Scholar 

  16. Kaleem, Z., Khaliq, M. Z., Khan, A., Ahmad, I., & Duong, T. Q. (2018). PS-CARA: Context-aware resource allocation scheme for mobile public safety networks. Sensors, 18(5), 1473.

    Article  Google Scholar 

  17. Kaleem, Z., Li, Y., & Chang, K. (2016). Public safety users priority-based energy and time-efficient device discovery scheme with contention resolution for ProSe in third generation partnership project long-term evolution-advanced systems. IET Communications, 10(15), 1873–1883.

    Article  Google Scholar 

  18. KIM, M., & TAKADA, J. i. (2009). Design and implementation of a cognitive radio based emergency sensor network in disaster area. IEICE technical report

  19. Kliks, A., Musznicki, B., Kowalik, K., & Kryszkiewicz, P. (2018). Perspectives for resource sharing in 5g networks. Telecommunication Systems, 68(4), 605–619.

    Article  Google Scholar 

  20. Lu, X., Ni, L., Jin, S., Wen, C. K., & Lu, W. J. (2017). SDR implementation of a real-time testbed for future multi-antenna smartphone applications. IEEE Access, 5, 19761–19772.

    Article  Google Scholar 

  21. Moriyama, M., & Fujii, T. (2015). Novel timing synchronization technique for public safety communication systems employing heterogeneous cognitive radio. In International conference on computing, networking and communications (ICNC) (pp. 325–330). IEEE

  22. Năstase, C.V., Marţian, A., Vlădeanu, C., & Marghescu, I. (2018). Spectrum sensing based on energy detection algorithms using GNU radio and USRP for cognitive radio. In 2018 International conference on communications (COMM) (pp. 381–384). IEEE

  23. Nussbaum, D., Kalfallah, K., Knopp, R., Moy, C., Nafkha, A., Leray, P., Delorme, M., Palicot, J., Martin, J., Clermidy, F., et al. (2009). Open platform for prototyping of advanced software defined radio and cognitive radio techniques. In 2009 12th Euromicro conference on digital system design, architectures, methods and tools (pp. 435–440). IEEE

  24. Palkovic, M., Raghavan, P., Li, M., Dejonghe, A., Van der Perre, L., & Catthoor, F. (2010). Future software-defined radio platforms and mapping flows. IEEE Signal Processing Magazine, 27(2), 22–33.

    Article  Google Scholar 

  25. Politis, C., Maleki, S., Duncan, J. M., Krivochiza, J., Chatzinotas, S., & Ottesten, B. (2018). SDR implementation of a testbed for real-time interference detection with signal cancellation. IEEE Access, 6, 20807–20821.

    Article  Google Scholar 

  26. Ramacher, U. (2007). Software-defined radio prospects for multistandard mobile phones. Computer, 40(10), 62–69.

    Article  Google Scholar 

  27. Schulte, M., Glossner, J., Mamidi, S., Moudgill, M., & Vassiliadis, S. (2004) A low-power multithreaded processor for baseband communication systems. In International workshop on embedded computer systems (pp. 393–402). Springer

  28. Shakoor, S., Kaleem, Z., Baig, M. I., Chughtai, O., Duong, T. Q., & Nguyen, L. D. (2019). Role of UAVs in public safety communications: Energy efficiency perspective. IEEE Access, 7, 140665–140679.

    Article  Google Scholar 

  29. Tan, K., Liu, H., Zhang, J., Zhang, Y., Fang, J., & Voelker, G. M. (2011). Sora: High-performance software radio using general-purpose multi-core processors. Communications of the ACM, 54(1), 99–107.

    Article  Google Scholar 

  30. Truong, D. N., Cheng, W. H., Mohsenin, T., Yu, Z., Jacobson, A. T., Landge, G., et al. (2009). A 167-processor computational platform in 65 nm CMOS. IEEE Journal of Solid-State Circuits, 44(4), 1130–1144.

    Article  Google Scholar 

  31. USA, N.I. (2015). USRP software defined radio device

  32. Woh, M., Lin, Y., Seo, S., Mahlke, S., Mudge, T., Chakrabarti, C., Bruce, R., Kershaw, D., Reid, A., Wilder, M., et al. (2008). From soda to scotch: The evolution of a wireless baseband processor. In 2008 41st IEEE/ACM International symposium on microarchitecture (pp. 152–163). IEEE

  33. Young, A. R., & Bostian, C. W. (2013). Simple and low-cost platforms for cognitive radio experiments [application notes]. IEEE Microwave Magazine, 14(1), 146–157.

    Article  Google Scholar 

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Authors and Affiliations

  1. National Radio and Telecommunication Corporation (NRTC), Haripur, Pakistan

    Imtiyaz Ali, Safiullah Khan & Muhammad Abrahim Satti

  2. Electrical and Computer Engineering Department, COMSATS University Islamabad, Wah Campus, Wah Cantt., Pakistan

    Zeeshan Kaleem & Zahoor Uddin

Authors
  1. Imtiyaz Ali
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  2. Zeeshan Kaleem
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  3. Safiullah Khan
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  4. Muhammad Abrahim Satti
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  5. Zahoor Uddin
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Correspondence to Zeeshan Kaleem.

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Ali, I., Kaleem, Z., Khan, S. et al. Cognitive radios real-time implementation on software defined radio for public safety communications. Telecommun Syst 74, 103–111 (2020). https://doi.org/10.1007/s11235-019-00641-0

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  • DOI: https://doi.org/10.1007/s11235-019-00641-0

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