Deep Reinforcement Learning for Network Selection over Heterogeneous Health Systems

Smart health systems improve our quality oflife by integrating diverse information and technologies into health and medical practices. Such technologies can significantly improve the existing health services. However, reliability, latency, and limited networks resources are among the many challenges hindering the realization of smart health systems. Thus, in this paper, we leverage the dense heterogeneous network (HetNet) architecture over 5 G network to enhance network capacity and provide seamless connectivity for smart health systems. However, network selection in HetNets is still a challenging problem that needs to be addressed. Inspired by the success of Deep Reinforcement Learning (DRL) in solving complicated control problems, we present a novel DRL model for solving the network selection problem with the aim of optimizing medical data delivery over heterogeneous health systems. Specifically, we formulate an optimization model that integrates the network selection problem with adaptive compression, at the network edge, to minimize the transmission energy consumption and latency, while meeting diverse applications' Quality of service (QoS) requirements. Our experimental results show that the proposed DRL-based model could minimize the energy consumption and latency compared to the greedy techniques, while meeting different users' demands in high dynamics environments.