Abstract
Heterogeneous network (HetNet) is the main networking form of the fifth-generation mobile communication system. In this paper, we propose a heterogeneous network resource management algorithm based on deep reinforcement learning (DRL). This algorithm uses deep Q-network (DQN) to solve resource allocation problem in heterogeneous network. This algorithm encourages the use of green energy to power the base station as much as possible, minimizing the use of the power grid to power the base station and achieve maximum energy efficiency. The simulation results show that this algorithm has efficient learning ability, can effectively improve the energy efficiency of the network, thereby realize great resource management.
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Zheng, W., Fang, J., Yuan, S., Guo, D., Zhang, Y. (2019). Resource Allocation in HetNets with Green Energy Supply Based on Deep Reinforcement Learning. 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_68
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DOI: https://doi.org/10.1007/978-3-030-37429-7_68
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