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Enhancing localization accuracy of collaborative cognitive radio users by internal noise mitigation

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Abstract

Location information of mobile primary users is one of the essential requirements for an underlay cognitive radio user to utilize the licensed spectrum efficiently. The performance of various location-based applications such as global navigation satellite system, device to device communication in dense urban 5G network also depends on the localization accuracy. In this paper, a collaborative localization scheme based on received signal strength has been proposed. The weighted centroid localization algorithm has been applied in the proposed network scenario to compute location coordinates of the mobile primary user. Since the channel noise effects are random and unavoidable, this paper has focused on the mitigation of the internal noise by designing a suitable reconfigurable FIR filter after the demodulator stage of a cognitive radio receiver circuit to improve precision of signal measurement during primary user localization. The localization error rate has come down to (1.3–1.62) % after internal noise mitigation. The enhancement in the localization accuracy improves the overall spectrum utilization efficiency and reduces the miss detection and false detection probabilities in the proposed underlay network.

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References

  1. Mitola, J. (2000). Cognitive radio an integrated agent architecture for software defined radio. Doctoral dissertation. Royal Institute of Technology (KTH) Stockholm, Sweden.

  2. 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 

  3. Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Noroozi, A., Navebi, M. M., & Amiri, R. (2019). Target localization in distributed MIMO radar from time delays, Doppler shifts, Azimuth and elevation angles of arrival. In 27th Iranian Conference on Electrical Engineering, pp. 1498–1503.

  6. Zhang, X., Zhu, H., & Luo, X. (2018). MIDAR: Massive MIMO based detection and ranging. In IEEE Global Communications Conference, pp. 1–6.

  7. Amiri, R., Behnia, F., & Noroozi, A. (2019). Efficient joint moving target and antenna localization in distributed MIMO radars. IEEE Transactions on Wireless Communications, 18(9), 4425–4435.

    Article  Google Scholar 

  8. Vukmirović, N., Janjić, M., Djurić, P. M., & Erić, M. (2018). Position estimation with a millimeter-wave massive MIMO system based on distributed steerable phased antenna arrays. EURASIP Journal on Advances in Signal Processing, 2018(1), 1–17.

    Article  Google Scholar 

  9. Zhao, A., & Ren, Z. (2019). Multiple-input and multiple-output antenna system with self-isolated antenna element for fifth-generation mobile terminals. Microwave and Optical Technology Letters, 61(1), 20–27.

    Article  Google Scholar 

  10. Wen, F., Wymeersch, H., Peng, B., Tay, W. P., So, H. C., & Yang, D. (2019). A survey on 5G massive MIMO localization. Digital Signal Processing, 94, 21–28.

    Article  Google Scholar 

  11. Langendoen, K., & Reijers, N. (2003). Distributed localization in wireless sensor networks: A quantitative comparison. Computer Networks, 43(4), 499–518.

    Article  Google Scholar 

  12. He, T., Huang, C., Blum, B. M., Stankovic, J. A., & Abdelzaher, T. (2003). Range-free localization schemes for large scale sensor networks. In 9th annual international conference on mobile computing and networking, pp. 81–95.

  13. Gui, L., Val, T., Wei, A., & Taktak, S. (2014). An adaptive range-free localisation protocol in wireless sensor networks. International Journal of Ad Hoc and Ubiquitous Computing, 15(1/2/3), 38–56.

    Article  Google Scholar 

  14. Kumar, P., Reddy, L., & Varma, S. (2009). Distance measurement and error estimation scheme for RSSI based localization in wireless sensor networks. In Fifth international conference on wireless communication and sensor networks, pp. 1–4.

  15. Kovavisaruch, L. O., & Ho, K. C. (2005). Alternate source and receiver location estimation using TDOA with receiver position uncertainties. In IEEE international conference on acoustics, speech, and signal processing, Vol. 4, pp. 1065–1068.

  16. Alippi, C., & Vanini, G. (2006). A RSSI-based and calibrated centralized localization technique for wireless sensor networks. In Fourth annual IEEE international conference on pervasive computing and communications workshops, pp. 5–10.

  17. Niculescu, D., & Nath, B. (2003). Ad Hoc positioning system (APS) using AOA. In Twenty-second annual joint conference of the ieee computer and communications societies, Vol. 3, pp. 1734–1743.

  18. Cheng, E., Lin, X., Chen, S., & Yuan, F. (2016). A TDoA localization scheme for underwater sensor networks with use of multi linear chirp signals. In Mobile Information Systems, Vol. 2016, pp. 1–11.

  19. Sayed, A. H., Tarighat, A., & Khajehnouri, N. (2005). Network-based wireless location: Challenges faced in developing techniques for accurate wireless location information. IEEE Signal Processing Magazine, 22(4), 24–40.

    Article  Google Scholar 

  20. Luo, Q., Peng, Y., Peng, X., & Saddik, A. (2014). Uncertain data clustering-based distance estimation in wireless sensor networks. Sensors, 14(4), 6584–6605.

    Article  Google Scholar 

  21. Liu, S., Chen, Y., Trappe, W., & Greenstein, L. J. (2009). Non-interactive localization of cognitive radios based on dynamic signal strength mapping. In Sixth international conference on wireless on-demand network systems and services, pp. 85–92.

  22. Sahoo, P. K., & Hwang, I. (2011). Collaborative localization algorithms for wireless sensor networks with reduced localization error. Sensors, 11(10), 9989–10009.

    Article  Google Scholar 

  23. Stoyanova, T., Kerasiotis, F., Efstathiou, K., & Papadopoulos, G. (2010). Modeling of the RSS uncertainty for RSS-based outdoor localization and tracking applications in wireless sensor networks. In Fourth International Conference on Sensor Technologies and Applications, pp. 45–50.

  24. Ye, F., Zhang, X., Li, Y., & Huang, H. (2016). Primary user localization algorithm based on compressive sensing in cognitive radio networks. Algorithms, 9(25), 1–11.

    Google Scholar 

  25. Singh, A. K., & Singh, A. K. (2016). Range-based primary user localization in cognitive radio networks. Procedia Computer Science, 93, 199–206.

    Article  Google Scholar 

  26. Lee, Y. D., & Koo, I. (2014). A received signal strength-based primary user localization scheme for cognitive radio sensor networks using underlay model-based spectrum access. KSII Transactions on Internet and Information Systems, 8(8), 2663–2674.

    Google Scholar 

  27. Saeed, N., Nam, H., Al-Naffouri, T. Y., & Alouini, M. S. (2019). Primary user localization and its error analysis in 5G cognitive radio networks. Sensors, 19(9), 1–12.

    Article  Google Scholar 

  28. Tandra, R., & Sahai, A. (2005). Fundamental limits on detection in low SNR under noise uncertainty. In international conference on wireless networks, communications and mobile computing, Vol. 1, pp. 464–469.

  29. Min, A. W., & Shin, K. G. (2012). Robust tracking of small-scale mobile primary user in cognitive radio networks. IEEE Transactions on Parallel and Distributed Systems, 24(4), 778–788.

    Article  Google Scholar 

  30. Jing, C., Sun, T., Chen, Q., Du, M., Wang, M., Wang, S., et al. (2019). A robust noise mitigation method for the mobile RFID location in built environment. Sensors, 19(9), 1–16.

    Article  Google Scholar 

  31. Giorgetti, A., Chiani, M., Dardari, D., Piesiewicz, R., & Bruck, G. H. (2008). The cognitive radio paradigm for ultra-wideband systems: The European Project EUWB. Ultra-Wideband, 2, 169–172.

    Google Scholar 

  32. Si, W., Selvakennedy, S., & Zomaya, A. Y. (2010). An overview of channel assignment methods for multi-radio multi-channel wireless mesh networks. Journal of Parallel and Distributed Computing, 70(5), 505–524.

    Article  Google Scholar 

  33. Kennedy, G., Davis, B., & Prasanna, S. R. M. (1985). Electronic communication systems, 20 (21). New Delhi: Tata McGraw-Hill Publishing Co., Ltd.

    Google Scholar 

  34. Wambacq, P., & Sansen, W. M. (2013). Distortion analysis of analog integrated circuits (Vol. 451). Springer Science & Business Media, ISBN 978-1-4419-5044-4.

  35. Neu, T. (2015). Direct RF conversion: From vision to reality. Texas Instruments Incorporated.

  36. Quan, Z., Cui, S., & Sayed, A. H. (2008). Optimal linear cooperation for spectrum sensing in cognitive radio networks. IEEE Journal of Selected Topics in Signal Processing, 2(1), 28–40.

    Article  Google Scholar 

  37. Liu, X., Jia, M., & Tan, X. (2013). Threshold optimization of cooperative spectrum sensing in cognitive radio networks. Radio Science, 48(1), 23–32.

    Article  Google Scholar 

  38. Atapattu, S., Tellambura, C., & Jiang, H. (2011). Spectrum sensing via energy detector in low SNR. In IEEE International Conference on Communications, pp. 1–5.

  39. Zhang, W., Mallik, R. K., & Letaief, K. B. (2009). Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks. IEEE Transactions on Wireless Communications, 8(12), 5761–5766.

    Article  Google Scholar 

  40. Marcum, J. I. (1950). Table of Q functions. Rand Corporation, Santa Monica, CA, U.S. Air Force Project RAND Research Memorandum M-339, ASTIA Document AD 1165451.

  41. Morales-Jimenez, D., Lopez-Martinez, F. J., Martos-Naya, E., Paris, J. F., & Lozano, A. (2014). Connections between the generalized Marcum Q-function and a class of hypergeometric functions. IEEE Transactions on Information Theory, 60(2), 1077–1082.

    Article  Google Scholar 

  42. Gil, A., Segura, J., & Temme, N. M. (2014). Algorithm 939: Computation of the Marcum Q-function. ACM Transactions on Mathematical Software (TOMS), 40(3), 1–21.

    Article  Google Scholar 

  43. Sithamparanathan, K., & Giorgetti, A. (2012). Cognitive radio techniques: Spectrum sensing, interference mitigation, and localization. Artech House, ISBN: 9781608072040.

  44. Lee, W. C. (1993). Mobile communications design fundamentals. Hoboken: Wiley. ISBN 978-0-471-57446-0.

    Book  Google Scholar 

  45. Parameswaran, A. T., Husain, M. I., & Upadhyaya, S. (2009). Is RSSI a reliable parameter in sensor localization algorithms: An experimental study. In Field Failure Data Analysis Workshop (F2DA09) (Vol. 5).

  46. Seybold, J. S. (2005). Introduction to RF propagation. Hoboken: Wiley. ISBN 9780471655961.

  47. Friis, H. T. (1946). A note on a simple transmission formula. Proceedings of the IRE, 34(5), 254–256.

    Article  Google Scholar 

  48. Rappaport, T. S. (1996). Wireless communications: Principles and practice. Upper Saddle River, NJ: Prentice Hal, ISBN: 0133755363.

  49. HE Yan Li. (2011). Research on centroid localization algorithm for wireless sensor networks based RSSI. Computer Simulation, 5, 165–168.

    Google Scholar 

  50. Bulusu, N., Heidemann, J., & Estrin, D. (2000). GPS-less low-cost outdoor localization for very small devices. IEEE Personal Communications, 7(5), 28–34.

    Article  Google Scholar 

  51. Blumenthal, J., Grossmann, R., Golatowski, F., & Timmermann, D. (2007). Weighted centroid localization in zigbee-based sensor networks. In International symposium on intelligent signal processing, pp. 1–6.

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Authors and Affiliations

  1. Heritage Institute of Technology, Kolkata, India

    Sabyasachi Chatterjee & Prabir Banerjee

  2. Jadavpur University, Kolkata, India

    Mita Nasipuri

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  1. Sabyasachi Chatterjee
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  2. Prabir Banerjee
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  3. Mita Nasipuri
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Correspondence to Sabyasachi Chatterjee.

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Chatterjee, S., Banerjee, P. & Nasipuri, M. Enhancing localization accuracy of collaborative cognitive radio users by internal noise mitigation. Telecommun Syst 76, 187–206 (2021). https://doi.org/10.1007/s11235-020-00708-3

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