Abstract
P-IoT is an IoT transmission system proposed for users in the private network industry, which has the advantages of high reliability, high security and strong coverage capability compared with the existing IoT system, it has great application potential. However, there is a amount of unused spectrum in sub-1GHz private network band now. How P-IoT can exploit the spectrum holes has become a hot research issue. In order to improve the utilization rate of spectrum resources, P-IoT can obtain the information about whether the spectrum is be used, and about network system which is using the frequency band through signal detection and system recognition. This can provide supports for P-IoT to formulate the utilization strategy of idle spectrum. In this paper, signal detection and system recognition technology of P-IoT are studied. This paper selects energy detection to detect narrowband private network signals and recognize the broad/narrowband private network signals. Taking the modulation modes of TETRA, PDT and dPMR as the classification objects, this paper also implements system recognition algorithms including binary trees and neural network classifiers, and further compares and analyzes the proposed algorithms. The simulation results show that the proposed recognition methods for P-IoT can effectively recognize the three private network signals. The results of this paper provide reference and support for the subsequent works that utilize unused spectrum, such as spectrum allocation and spectrum collaboration.
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References
Guo, Y., Xie, X., Qin, C., Wang, Y.: Fog computing federated learning system framework for smart healthcare. In: CSCW 2021. CCIS, vol 1491. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-4546-5_11
Wang, R., Zhao, L.: Application of anti-collision early warning system for 5g internet of vehicles. In: Hung, J.C., Chang, J.-W., Pei, Y., Wei-Chen, Wu. (eds.) Innovative Computing. LNEE, vol. 791, pp. 677–684. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-4258-6_84
Thandavarayan, G., Sepulcre, M., Gozalvez, J.: Cooperative perception for connected and automated vehicles: evaluation and impact of congestion control. IEEE Access 8, 197665–197683 (2020)
Miao, Y., Shao, B., Xie, W.: Exploration of the application of private Internet of Things in broadband converged networks. Police Technol., 12–15 (2017)
Sun, P., Song, Z., Yu, Y.: Research on private-internet of things technology. Mobile Commun. 42(07), 92–96 (2018)
Sun, P., Yu, Y., Wang, Y.: Innovative application of private-internet of things in emergency field. Mobile Commun. 43(03), 12–17 (2019)
Wen, W., Mendel, J.M.: Maximum-likelihood classification for digital amplitude-phase modulations. IEEE Trans. Commun. 48(2), 189–193 (2000)
Donoho, D.L., Huo, X.: Large-Sample modulation classification using hellinger representation. In: 1997 First IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications (1997)
Zhu, X., Lin, Y., Dou, Z.: Automatic recognition of communication signal modulation based on neural network. In: 2016 IEEE International Conference on Electronic Information and Communication Technology (ICEICT) (2017)
GB/T 15539–1995. Technical specifications for trunked mobile radio systems (1995)
GA/T 1056–2013. Technical specifications for Police Digital Trunking (PDT) communication system (2013)
ETSI TS 102 658 V2.5.1. Digital Private Mobile Radio (dPMR) using FDMA with a channel spacing of 6,25 kHz (2015)
O’Shea, T.J., Johnathan Corgan, T., Clancy, C.: Convolutional radio modulation recognition networks. In: Jayne, C., Iliadis, L. (eds.) EANN 2016. CCIS, vol. 629, pp. 213–226. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44188-7_16
Zhao, X., Guo, C., Li, J.: Mixed recognition algorithm for signal modulation schemes by high-older cumulants and cyclic spectrum. J. Electron. Inf. Technol. 38(3), 674–680 (2016)
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Jiang, J., Wang, B., Sun, P., Li, B. (2024). Research on Signal Detection and System Recognition Techniques in Private Internet of Things. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2012. Springer, Singapore. https://doi.org/10.1007/978-981-99-9637-7_39
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DOI: https://doi.org/10.1007/978-981-99-9637-7_39
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