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• W2783130023 abstract "Recent advances in networking, caching and computing technologies can have great impacts on the developments of green heterogeneous wireless networks, where different sizes of cells co-exist. Nevertheless, these important enabling technologies have traditionally been studied separately in the existing works on wireless networks. In this paper, we propose an integrated framework that can enable dynamic orchestration of networking, caching and computing resources to improve the performance of green heterogeneous wireless networks. We use an energy-efficient caching strategy based on storing maximum-distance separable (MDS) encoded packets. The resource allocation strategy in this framework is formulated as a joint optimization problem. The decision on how to allocate the dynamic resources is very complicated when considering networking, caching and computing. Therefore, we propose a novel deep reinforcement learning approach, which can effectively handle systems with large complexity. In addition, we use Google TensorFlow to implement deep reinforcement learning. Simulation results with different system parameters are presented to show the effectiveness of the proposed scheme." @default.
• W2783130023 created "2018-01-26" @default.
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• W2783130023 date "2017-12-01" @default.
• W2783130023 modified "2023-10-10" @default.
• W2783130023 title "A Big Data Deep Reinforcement Learning Approach to Next Generation Green Wireless Networks" @default.
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• W2783130023 doi "https://doi.org/10.1109/glocom.2017.8254717" @default.
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