Abstract
In order to further improve the accurate detection signal, reduce interference between signals, this paper designs a new type of signal detection algorithm for satellite communication systems, using stochastic resonance technology improve the signal-to-noise ratio of the input signal, the signal by using energy detection, double threshold, accurate judgment. The first step in the conventional energy of double threshold detection, the second step into the energy detection method based on stochastic resonance detection process. The experimental results show that this algorithm under the condition of low SNR signals effectively detect, promoted the whole satellite communication system performance.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
He, D., He, C., Jiang, L.: Spectrum sensing approach based on optimal stochastic resonance technique under color noise background in cognitive radio networks. In: 2010 IEEE International Conference on Communications Workshops (ICC), pp. 1–4 (2010)
Lin, Y., He, C., Jiang, L.: A cyclostationary-based spectrum sensing method using stochastic resonance in cognitive radio. In: IEEE International Conference on Communications Workshops (ICC), pp. 1–5 (2010)
Chen, H., Varshney, P.K.: Theory of the stochastic resonance effect in signal detection—part II: variable detectors. IEEE Trans. Sig. Process. 56(10), 5031–5041 (2008)
He, D., Lin, Y.P., He, C.: A novel spectrum sensing technique in cognitive radio based on stochastic resonance. IEEE Trans. Veh. Technol. 59(4), 1680–1688 (2010)
Jia, M., Gu, X., Guo, Q., Xiang, W., Zhang, N.: Broadband hybrid satellite-terrestrial communication systems based on cognitive radio toward 5G. IEEE Wirel. Commun. 23(6), 96–106 (2016)
Jia, M., Liu, X., Gu, X., Guo, Q.: Joint cooperative spectrum sensing and channel selection optimization for satellite communication systems based on cognitive radio. Int. J. Satell. Commun. Netw. 35(2), 139–150 (2017)
Jiang, X.L., Gu, X.M.: Double threshold collaborative spectrum sensing algorithm based on energy detection. J. Hei LongJiang Univ. Sci. Technol. 553–556 (2016)
Chen, H., Varshney, P.K., Kay, S.M.: Theory of the stochastic resonance effect in signal detection: part I—fixed detectors. IEEE Trans. Sig. Process. 55(7), 3172–3184 (2007)
Kawaguchi, M., Mino, H., Momose, K., Durand, D.M.: Stochastic resonance with a mixture of sub-and supra-threshold stimuli in a population of neuron models. In: IEEE International Conference on Engineering in Medicine and Biology Society (EMBC), pp. 7328–7331 (2011)
Wu, J.: A cooperative double-threshold energy detection algorithm in cognitive radio systems. In: IEEE International Conference on Wireless Communications Networking and Mobile Computing (2009)
Liang, Y.C., Zeng, Y.: Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans. Wirel. Commun. 7(4), 1326–1337 (2008)
Peh, E.C.Y., Liang, Y.C., Guan, Y.L.: Optimization of cooperative sensing in cognitive radio networks: a sensing-throughput tradeoff view. IEEE Trans. Veh. Technol. 58(9), 5294–5299 (2009)
Acknowledgement
This work is supported by Heilongjiang Natural Science Fund Project (F2015019, F2015017) (2017RAXXJ055), and Heilongjiang Provincial Postdoctoral Fund Project (LBH-Z16054).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Jiang, X., Diao, M. (2019). A New Type Double-Threshold Signal Detection Algorithm for Satellite Communication Systems Based on Stochastic Resonance Technology. In: Jia, M., Guo, Q., Meng, W. (eds) Wireless and Satellite Systems. WiSATS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 281. Springer, Cham. https://doi.org/10.1007/978-3-030-19156-6_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-19156-6_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19155-9
Online ISBN: 978-3-030-19156-6
eBook Packages: Computer ScienceComputer Science (R0)