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Resource Allocation Using Gradient Boosting Aided Deep Q-Network for IoT in C-RANs
[article]
2019
arXiv
pre-print
In this paper, we investigate dynamic resource allocation (DRA) problems for Internet of Things (IoT) in real-time cloud radio access networks (C-RANs), by combining gradient boosting approximation and deep reinforcement learning to solve the following two major problems. Firstly, in C-RANs, the decision making process of resource allocation is time-consuming and computational-expensive, motivating us to use an approximation method, i.e. the gradient boosting decision tree (GBDT) to approximate
arXiv:1910.13084v1
fatcat:hghsr4ovwzfm3paygzirxxqwgi