Channel access and power control for energy-efficient delay-aware heterogeneous cellular networks for smart grid communications using deep reinforcement learning

Cellular technology with long-term evolution (LTE)-based standards is a preferable choice for smart grid neighborhood area networks due to its high availability and scalability. However, the integration of cellular networks and smart grid communications puts forth a significant challenge due to the simultaneous transmission of real-time smart grid data which could cause radio access network (RAN) congestions. Heterogeneous cellular networks (HetNets) have been proposed to improve the performance of LTE because HetNets can alleviate RAN congestions by off-loading access attempts from a macrocell to small cells. In this paper, we study energy efficiency and delay problems in HetNets for transmitting smart grid data with different delay requirements. We propose a distributed channel access and power control scheme, and develop a learning-based approach for the phasor measurement units (PMUs) to transmit data successfully by considering interference and signal-to-interference-plus-noise ratio (SINR) constraints. In particular, we exploit a deep reinforcement learning(DRL)-based method to train the PMUs to learn an optimal policy that maximizes the earned reward of successful transmissions without having knowledge on the system dynamics. Results show that the DRL approach obtains good performance without knowing the system dynamic beforehand and outperforms the Gittin index policy in different normal ratios, minimum SINR requirements and number of users in the cell