Abstract
In view of the current energy problems of communication base station, a multi-base station energy cooperation strategy is proposed to reduce the energy consumption of power grid, which is introducing renewable energy and energy cooperation between the base station based on the Nash-Q learning algorithm. We analyze the packet rate and throughput of the system under the proposed approach. The simulation results show that the proposed algorithm can enhances the adaptability to the changing environment, effectively improve the system capacity.
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References
Zhang, H., Dong, Y., Cheng, J., Hossain, M.J., Leung, V.C.M.: Fronthauling for 5G LTE-U ultra dense cloud small cell networks. IEEE Wirel. Commun. 23(6), 48–53 (2016)
Zhang, H., Liu, N., Chu, X., Long, K., Aghvami, A., Leung, V.: Network slicing based 5G and future mobile networks: mobility, resource management, and challenges. IEEE Commun. Mag. 55(8), 138–145 (2017)
Tombaz, S., Vastberg, A., Zander, J.: Energy-and cost-efficient ultrahigh-capacity wireless accross. IEEE Wirel. Commun. 18(5), 18–24 (2011)
Gong, J., Zhou, S., Niu, Z.: Optimal power allocation for energy harvesting and power grid coexisting wireless communication systems. IEEE Trans. Commun. 61(7), 3040–3049 (2013)
Pol Blasco, D., Dohler, M.: A learning theoretic approach to energy harvesting communication system optimization. IEEE Trans. Wirel. Commun. 12(4), 1872–1882 (2013)
Lin, W.T., Lai, I.W., Lee, C.H.: Distributed energy cooperation for energy harvesting nodes using reinforcement learning. In: 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Hong Kong, pp. 1584–1588 (2015)
Udenze, A., McDonald-Msier, K.: Discrete time markov based optimisation for dynamic control of transmitter power in wireless sensor networks. In: IET Proceedings of the 3rd UK Embedded Forum, Newcastle, UK, 2–3 April (2007)
Alhajry, M., Alvi, F., Ahmed, M.: TD(λ) and q-learning based ludo players. In: IEEE Conference on Computation Intelligence and Games (CIG), pp. 83–90 (2012)
Li, W., Zhang, H., Zheng, W., Su, T., Wen, X.: Energy-efficient power allocation with dual-utility in two-tier OFDMA femtocell. In: Proceedings of IEEE Globecom (2012)
Liu, H., Zheng, W., Zhang, H., Zhang, Z., Wen, X.: An iterative two-step algorithm for energy efficient resource allocation in multi-Cell OFDMA networks. In: Proceedings of IEEE WCNC (2013)
Ma, W., Zheng, W., Zhang, H., Wen, X.: MOS-driven energy efficient power allocation for wireless video communications. In: Proceedings of IEEE Globecom (2012)
Zhang, Z., Zhang, H., Lu, Z., Zhao, Z., Wen, X.: Energy-efficient resource optimization in OFDMA-based dense femtocell networks. In: Proceedings of IEEE ICT (2013)
Dayan, W.P.: Q-Learning. Mach. Learn. 38(2), 362–399 (2002)
Hu, J.L., Wellmam, M.P.: Multiagent reinforcement learning: theoretical framework and an algorithm. In: 15th International Conference on Machine Learning, Washington, pp. 242–249 (1999)
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Lv, Y., Li, B., Zhao, W., Guo, D., Yao, Y. (2018). Multi-base Station Energy Cooperation Based on Nash Q-Learning Algorithm. In: Long, K., Leung, V., Zhang, H., Feng, Z., Li, Y., Zhang, Z. (eds) 5G for Future Wireless Networks. 5GWN 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-319-72823-0_7
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DOI: https://doi.org/10.1007/978-3-319-72823-0_7
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