User scheduling and slicing resource allocation in industrial Internet of Things

Heterogeneous base station deployment enables to provide high capacity and wide area coverage. Network slicing makes it possible to allocate wireless resource for heterogeneous services on demand. These two promising technologies contribute to the unprecedented service in 5G. We establish a multiservice heterogeneous network model, which aims to raise the transmission rate under the delay constraints for active control terminals, and optimize the energy efficiency for passive network terminals. A policy-gradient-based deep reinforcement learning algorithm is proposed to make decisions on user association and power control in the continuous action space. Simulation results indicate the good convergence of the algorithm, and higher reward is obtained compared with other baselines.