  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 the solutions of second order cone
programming (SOCP) problem. Moreover, considering the innumerable states in
real-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 status
of remote radio heads (RRHs). We propose a GBDT-based DQN framework for the DRA
problem, where the heavy computation to solve SOCP problems is cut down and
great power consumption is saved in the whole C-RAN system. We demonstrate that
the generated policy is error-tolerant even the gradient boosting regression
may not be strictly subject to the constraints of the original problem.
Comparisons between the proposed method and existing baseline methods confirm
the advantages of our method.
