Abstract
Cognitive radio networks can efficiently manage the radio spectrum by utilizing the spectrum holes for secondary users in licensed frequency bands. The energy that is used to detect spectrum holes can be reduced considerably by predicting them. However, collisions can occur either between a primary user and secondary users or among the secondary users themselves. This paper introduces a centralized channel allocation algorithm (CCAA) in a scenario with multiple secondary users to control primary and secondary collisions. The proposed allocation algorithm, which uses a channel state predictor (CSP), provides good performance with fairness among the secondary users while they have minimal interference with the primary user. The simulation results show that the probability of a wrong prediction of an idle channel state in a multi-channel system is less than 0.9%. The channel state prediction saves the sensing energy by 73%, and the utilization of the spectrum can be improved by more than 77%.
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Bhattacharya A, Ghosh R, Sinha K, Datta D, Sinha BP (2015) Noncontiguous channel allocation for multimedia communication in cognitive radio networks. IEEE Trans Cognit Commun Netw 1(4):420–434 Dec
Sriharipriya KC, Sanju R (2017) Artifical neural network based multi dimensional spectrum sensing in full duplex cognitive radio networks. In: 2017 international conference on computing methodologies and communication (ICCMC) July 2017, pp. 307–312
Zhang M, Diao M, Guo L (2017) Convolutional neural networks for automatic cognitive radio waveform recognition. IEEE Access 5:11074–11082
Melián-Gutiérrez L, Modi N, Moy C, Bader F, Pérez-Álvarez I, Zazo S (2015) Hybrid ucb-hmm: a machine learning strategy for cognitive radio in hf band. IEEE Trans Cognit Commun Netw 1(3):347–358 Sep
Lee W (2018) Resource allocation for multi-channel underlay cognitive radio network based on deep neural network. IEEE Commun Lett 22(9):1942–1945 Sep
Jiang W, Schotten HD (2019) Recurrent neural network-based frequency-domain channel prediction for wideband communications. In: 2019 IEEE 89th vehicular technology conference (VTC2019-Spring), pp 1–6
Shamsi N, Mousavinia A, Amirpour H (2013) A channel state prediction for multi-secondary users in a cognitive radio based on neural network. In: 2013 international conference on electronics, computer and computation (ICECCO), Nov 2013, pp 200–203
Sun J, Liu X, Ren G, Jia M, Guo Q (2019) A spectrum prediction technique based on convolutional neural networks. In: Jia M, Guo Q, Meng W (eds) Wireless and satellite systems. Springer International Publishing, Cham, pp 69–77
Tumuluru VK, Wang P, Niyato D (2010) A neural network based spectrum prediction scheme for cognitive radio. In: 2010 IEEE international conference on communications, May 2010, pp 1–5
Huk M, Mizera-Pietraszko J (2015) Contextual neural-network based spectrum prediction for cognitive radio. In: 2015 fourth international conference on future generation communication technology (FGCT), July 2015, pp 1–5
Yang J, Zhao H, Chen X, Xu J, Zhang J (2014) Energy-efficient design of spectrum prediction in cognitive radio networks: prediction strategy and communication environment. In: 2014 12th international conference on signal processing (ICSP), Oct 2014, pp 154–158
Acknowledgements
This research has been supported in part by the Christian Doppler Laboratory ATHENA (https://athena.itec.aau.at/).
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Shams, N., Amirpour, H., Timmerer, C., Ghanbari, M. (2022). A Channel Allocation Algorithm for Cognitive Radio Users Based on Channel State Predictors. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 236. Springer, Singapore. https://doi.org/10.1007/978-981-16-2380-6_62
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DOI: https://doi.org/10.1007/978-981-16-2380-6_62
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