Abstract
Radio scenario recognition is critically important to acquire comprehensive situation awareness for cognitive radio networks in the millimeter-wave bands, especially for dense small cell environment. In this paper, a generic framework of machine learning-aided radio scenario recognition scheme is proposed to acquire the environmental awareness. Particularly, an advanced back propagation neural network-based AdaBoost classification algorithm is developed to recognize various radio scenarios, in which different channel conditions such as line-of-sight (LOS), non-line-of-sight (NLOS), and obstructed line-of-sight (OLOS) are encountered by the desired signal or co-channel interference. Moreover, the advanced AdaBoost algorithm takes the offline training performance into account during the decision fusion. Simulation results show that machine learning can be exploited to recognize the complicated radio scenarios reliably and promptly.
This work is supported in part by Sony China Research Laboratory, Sony (China) Ltd. Prof. Zhao’s work is also supported in part by Beijing Natural Science Foundation (4172046).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
He, A., et al.: A survey of artificial intelligence for cognitive radios. IEEE Trans. Veh. Technol. 59(4), 1578–1592 (2010)
Zhao, H., Liu, Y., Zhu, X., Zhao, Y., Zha, H.: Scene understanding in a large dynamic environment through a laser-based sensing. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 127–133 (2010)
Liao, Y., Kodagoda, S., Wang, Y., Shi, L., Liu, Y.: Understand scene categories by objects: a semantic regularized scene classifier using convolutional neural networks. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2318–2325 (2016)
Harb, M., Abielmona, R., Naji, K., Petriu, E.: Neural networks for environmental recognition and navigation of a mobile robot. In: IEEE Conference on Instrumentation and Measurement Technology, pp. 1123–1128 (2008)
Tumuluru, V.K., Wang, P., Niyato, D.: A neural network based spectrum prediction scheme for cognitive radio. In: IEEE International Conference Communications (ICC), pp. 1–5, May 2010
Fehske, A., Gaeddert, J., Reed, J.H.: A new approach to signal classification using spectral correlation and neural networks. In: First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, pp. 144–150 (2005)
Kegen, Y., Dutkiewica, E.: NLOS identification and mitigation for mobile tracking. IEEE Trans. Aerosp. Electron. Syst. 49(3), 1438–1452 (2013)
Li, W., Zhang, T., Zhang, Q.: Experimental researches on an UWB NLOS identification method based on machine learning. In: 2013 IEEE International Conference on Communication Technology (ICCT), pp. 473–477 (2013)
Xiao, Z., Wen, H., Markham, A.: Non-line-of-sight identification and mitigation using received signal strength. IEEE Trans. Wireless Commun. 14(3), 1689–1702 (2015)
Freund, Y., Schapire, R.E.: A desicion-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-59119-2_166
Yilmaz, H.B., Tugcu, T., Alagöz, F., Bayhan, S.: Radio environment map as enabler for practical cognitive radio networks. IEEE Commun. Mag. 51(12), 162–169 (2013)
Lechun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Sulyman, A.I., Nassar, A.T., Samimi, M.K., MacCartney, G.R., Rappaport, T.S., Alsanie, A.: Radio propagation path loss models for 5G cellular networks in the 28 GHz and 38 GHz millimeter-wave bands. IEEE Commun. Mag. 52(9), 78–86 (2014)
Zhao, Y., Le, B., Reed, J.H.: Network support – the radio environment map. In: Fette, B. (ed.) Cognitive Radio Technology, pp. 325–366. Elsevier (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wang, J., Zhao, Y., Guo, X., Sun, C. (2018). Machine Learning-Aided Radio Scenario Recognition for Cognitive Radio Networks in Millimeter-Wave Bands. In: Marques, P., Radwan, A., Mumtaz, S., Noguet, D., Rodriguez, J., Gundlach, M. (eds) Cognitive Radio Oriented Wireless Networks. CrownCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 228. Springer, Cham. https://doi.org/10.1007/978-3-319-76207-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-76207-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76206-7
Online ISBN: 978-3-319-76207-4
eBook Packages: Computer ScienceComputer Science (R0)