Abstract
With the development of wireless communication technology and the lack of spectrum resources, it is very meaningful to study the dynamic spectrum allocation in the cognitive Internet of Things. In this paper, the system model is firstly established. In an underlay mode, considering the interference between primary and secondary users, jointing channel selection and power allocation, aiming to maximize the spectrum efficiency of all secondary users. Different from the traditional heuristic algorithm, the underlay-cognitive-radio-deep-Q-network frame-work (UCRDQN) based on deep reinforcement learning, is proposed to find the optimal solution efficiently. The simulation results show that the UCRDQN algorithm can achieve higher spectrum efficiency and is more stable and efficient than other algorithms.
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References
Agiwal, M., Roy, A., Saxena, N.: Next generation 5G wireless networks: a comprehensive survey. IEEE Commun. Surv. Tutor. 18(3), 1617–1655 (2016)
Radio, C.: Brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005)
Wenzhu, Z., Xuchen, L.: Centralized dynamic spectrum allocation in cognitive radio networks based on fuzzy logic and Q-learning. China Commun. 8(7), 46–54 (2011)
Wang, Y., Wang, Y., Zhou, F., Wu, Y., Zhou, H.: Resource allocation in wireless powered cognitive radio networks based on a practical non-linear energy harvesting model. IEEE Access 5, 17618–17626 (2017)
Yang, K., Wang, L., Wang, S., Zhang, X.: Optimization of resource allocation and user association for energy efficiency in future wireless networks. IEEE Access 5, 16469–16477 (2017)
Le, L.B., Hossain, E.: Resource allocation for spectrum underlay in cognitive radio networks. IEEE Trans. Wireless Commun. 7(12), 5306–5315 (2008)
Hoang, A.T., Liang, Y.C.: A two-phase channel and power allocation scheme for cognitive radio networks. In: 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1–5. IEEE, September 2006
Xu, D., Li, Q.: Joint power control and time allocation for wireless powered underlay cognitive radio networks. IEEE Wirel. Commun. Lett. 6(3), 294–297 (2017)
Sboui, L., Rezki, Z., Alouini, M.S.: Energy-efficient power allocation for underlay cognitive radio systems. IEEE Trans. Cogn. Commun. Netw. 1(3), 273–283 (2015)
Li, X., Fang, J., Cheng, W., Duan, H., Chen, Z., Li, H.: Intelligent power control for spectrum sharing in cognitive radios: a deep reinforcement learning approach. IEEE Access 6, 25463–25473 (2018)
Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529 (2015)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Zhang, X., Zhang, X., Han, L., Xing, R.: Utilization-oriented spectrum allocation in an underlay cognitive radio network. IEEE Access 6, 12905–12912 (2018)
Wang, S., Liu, H., Gomes, P.H., Krishnamachari, B.: Deep reinforcement learning for dynamic multichannel access in wireless networks. IEEE Trans. Cogn. Commun. Netw. 4(2), 257–265 (2018)
Zhang, Y., Kang, C., Ma, T., Teng, Y., Guo, D.: Power allocation in multi-cell networks using deep reinforcement learning. In: 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), pp. 1–6. IEEE, April 2019
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Zheng, W., Wu, G., Qie, W., Zhang, Y. (2019). Deep Reinforcement Learning for Joint Channel Selection and Power Allocation in Cognitive Internet of Things. In: Milošević, D., Tang, Y., Zu, Q. (eds) Human Centered Computing. HCC 2019. Lecture Notes in Computer Science(), vol 11956. Springer, Cham. https://doi.org/10.1007/978-3-030-37429-7_69
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DOI: https://doi.org/10.1007/978-3-030-37429-7_69
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