A Survey on Applications of Deep Reinforcement Learning in Resource Management for 5G Heterogeneous Networks

Heterogeneous networks (HetNets) have been regarded as the key technology for fifth generation (5G) communications to support the explosive growth of mobile traffics. By deploying small-cells within the macrocells, the HetNets can boost the network capacity and support more users especially in the hotspot and indoor areas. Nonetheless, resource management for such networks becomes more complex compared to conventional cellular networks due to the interference arise between small-cells and macrocells, which thus making quality of service provisioning more challenging. Recent advances in deep reinforcement learning (DRL) have inspired its applications in resource management for 5G HetNets. In this paper, a survey on the applications of DRL in resource management for 5G HetNets is conducted. In particular, we review the DRL-based resource management schemes for 5G HetNets in various domains including energy harvesting, network slicing, cognitive HetNets, coordinated multipoint transmission, and big data. An insightful comparative summary and analysis on the surveyed studies is provided to shed some light on the shortcomings and research gaps in the current advances in DRL-based resource management for 5G HetNets. Last but not least, several open issues and future directions are presented.