Abstract
We introduce a simple yet efficient framework for the localization and tracking of fixed-frequency and frequency-hopping (FH) wireless signals that coexist in a wide radio-frequency band. In this spectrum sensing scheme, an energy detector is applied to each Short-time Fourier Transform of the wideband signal to produce a binary spectrogram. Bounding boxes for narrowband signals are then identified by using image processing techniques on a block of the spectrogram at a time. These boxes are also tracked along the time axis and fused with the newly detected boxes to provide an on-line system for spectrum sensing. Fast and highly accurate detection is achieved in simulations for various FF signals and FH signals with different hopping patterns and speeds. In particular, for the SNR of 4 dB over a bandwidth of 50 MHz, 97.98% of narrowband signals were detected with average deviations of about \(0.02\,\mathrm{ms}\) in time and \(2.15\,\mathrm{KHz}\) in frequency.
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Notes
- 1.
In practice, due to the slow variation of the noise with time, the noise floor estimation can be done once for multiple signal blocks.
References
Ariananda, D.D., Leus, G.: Cooperative compressive wideband power spectrum sensing. In: Proceedings of IEEE ASILOMAR, pp. 303–307. Pacific Groove, CA, July 2012
Cohen, D., Eldar, Y.C.: Sub-Nyquist sampling for power spectrum sensing in cognitive radios: a unified approach. IEEE Trans. Signal Process. 62(15), 3897–3910 (2014)
Farhang-Boroujeny, B.: Filter bank spectrum sensing for cognitive radios. IEEE Trans. Signal Process. 56(5), 1801–1811 (2008)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Upper Saddle River (2006)
Lampert, T.A., O’keefe, S.E.M.: A survey of spectrogram track detection algorithms. Appl. Acoustics 71(2), 87–100 (2010)
Liang, Y.C., Chen, K.C., Li, G.Y., Mahonen, P.: Cognitive radio networking and communications: an overview. IEEE Trans. Veh. Technol. 60(7), 3386–3407 (2011)
Mishali, M., Eldar, Y.C.: Blind multiband signal reconstruction: compressive sensing for analog signals. IEEE Trans. Signal Process. 57(3), 993–1009 (2009)
Mitola, J., Maguire, G.Q.: Cognitive radio: making software radios more personal. IEEE Pers. Commun. 6(4), 13–18 (1999)
Ready, M.J., Downey, M.L., Corbalis, L.J.: Automatic noise floor spectrum estimation in the presence of signals. In: Proceedings of IEEE ASILOMAR, pp. 877–881 (1997)
Sun, H., Nallanathan, A., Wang, C.X.: Wideband spectrum sensing for cognitive radio networks: a survey. IEEE Wirel. Commun. 20(2), 74–81 (2013)
Tian, Z., Giannakis, G.B.: A wavelet approach to wideband spectrum sensing for cognitive radios. In: Proceeding of IEEE CROWNCOM, pp. 1–5, Mykonos Island, Greece, 08–10 July 2006
Tian, Z., Tafesse, Y., Sadler, B.M.: Cyclic feature detection with sub-Nyquist sampling for wideband spectrum sensing. IEEE J. Sel. Top. Signal Process. 6(1), 58–69 (2012)
Watson, C.M.: Signal detection and digital modulation classification-based spectrum sensing for cognitive radio. Ph.D. thesis, Northeastern University, Boston, MA, USA (2013)
Yucek, T., Arslan, H.: A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun. Surv. Tutor. 11(1), 116–130 (2009)
Zeng, F., Li, C., Tian, Z.: Distributed compressive spectrum sensing in cooperative multihop cognitive networks. IEEE J. Sel. Top. Signal Process. 5(1), 37–48 (2011)
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Nguyen, H.Q., Nguyen, H.P.K., Nguyen, B.T. (2019). An Image Processing Approach to Wideband Spectrum Sensing of Heterogeneous Signals. In: Moerman, I., Marquez-Barja, J., Shahid, A., Liu, W., Giannoulis, S., Jiao, X. (eds) Cognitive Radio Oriented Wireless Networks. CROWNCOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 261. Springer, Cham. https://doi.org/10.1007/978-3-030-05490-8_20
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