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Telecommunication Systems
Erschienen in: Telecommunication Systems 2/2021

10.08.2020

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

verfasst von: Sabyasachi Chatterjee, Prabir Banerjee, Mita Nasipuri

Erschienen in: Telecommunication Systems | Ausgabe 2/2021

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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.
Vorheriger Artikel Stochastic approach for channel selection in cognitive radio networks using optimization techniques
Nächster Artikel Research on a factor graph-based robust UWB positioning algorithm in NLOS environments

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Literatur
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.CrossRef
3.
Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.CrossRef
4.
Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
11.
Langendoen, K., & Reijers, N. (2003). Distributed localization in wireless sensor networks: A quantitative comparison. Computer Networks, 43(4), 499–518.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.
25.
Singh, A. K., & Singh, A. K. (2016). Range-based primary user localization in cognitive radio networks. Procedia Computer Science, 93, 199–206.CrossRef
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.
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.CrossRef
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.CrossRef
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.CrossRef
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.
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.CrossRef
33.
Kennedy, G., Davis, B., & Prasanna, S. R. M. (1985). Electronic communication systems, 20 (21). New Delhi: Tata McGraw-Hill Publishing Co., Ltd.
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.CrossRef
37.
Liu, X., Jia, M., & Tan, X. (2013). Threshold optimization of cooperative spectrum sensing in cognitive radio networks. Radio Science, 48(1), 23–32.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.
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.CrossRef
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.
Metadaten
Titel
Enhancing localization accuracy of collaborative cognitive radio users by internal noise mitigation
verfasst von
Sabyasachi Chatterjee
Prabir Banerjee
Mita Nasipuri
Publikationsdatum
10.08.2020
Verlag
Springer US
Erschienen in
Telecommunication Systems / Ausgabe 2/2021
Print ISSN: 1018-4864
Elektronische ISSN: 1572-9451
DOI
https://doi.org/10.1007/s11235-020-00708-3

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