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In this paper, we investigate dynamic resource allocation (DRA) problems forInternet of Things (IoT) in real-time cloud radio access networks (C-RANs), bycombining gradient boosting approximation and deep reinforcement learning tosolve the following two major problems. Firstly, in C-RANs, the decision makingprocess of resource allocation is time-consuming and computational-expensive,motivating us to use an approximation method, i.e. the gradient boostingdecision tree (GBDT) to approximate the solutions of second order coneprogramming (SOCP) problem. Moreover, considering the innumerable states inreal-time C-RAN systems, we employ a deep reinforcement learning framework,i.e., deep Q-network (DQN) to generate a robust policy that controls the statusof remote radio heads (RRHs). We propose a GBDT-based DQN framework for the DRAproblem, where the heavy computation to solve SOCP problems is cut down andgreat power consumption is saved in the whole C-RAN system. We demonstrate thatthe generated policy is error-tolerant even the gradient boosting regressionmay not be strictly subject to the constraints of the original problem.Comparisons between the proposed method and existing baseline methods confirmthe advantages of our method.

Resource Allocation Using Gradient Boosting Aided Deep Q-Network for IoT in C-RANs