Deep Reinforcement Learning for Mobile 5G and Beyond: Fundamentals, Applications, and Challenges

TLDR

Future‑generation wireless networks must support explosive data traffic, dense heterogeneous deployments, and diverse services, yet conventional resource‑management approaches fail under dynamic, uncertain conditions. This article investigates deep reinforcement learning as a means for autonomous, locally optimal decision‑making in 5G networks, exemplified by a slicing‑optimization application. The authors review DRL fundamentals, survey its use in 5G, and demonstrate a slicing‑optimization algorithm. Numerical results show the DRL approach outperforms baseline solutions.

Abstract

Future-generation wireless networks (5G and beyond) must accommodate surging growth in mobile data traffic and support an increasingly high density of mobile users involving a variety of services and applications. Meanwhile, the networks become increasingly dense, heterogeneous, decentralized, and ad hoc in nature, and they encompass numerous and diverse network entities. Consequently, different objectives, such as high throughput and low latency, need to be achieved in terms of service, and resource allocation must be designed and optimized accordingly. However, considering the dynamics and uncertainty that inherently exist in wireless network environments, conventional approaches for service and resource management that require complete and perfect knowledge of the systems are inefficient or even inapplicable. Inspired by the success of machine learning in solving complicated control and decision-making problems, in this article we focus on deep reinforcement- learning (DRL)-based approaches that allow network entities to learn and build knowledge about the networks and thus make optimal decisions locally and independently. We first overview fundamental concepts of DRL and then review related works that use DRL to address various issues in 5G networks. Finally, we present an application of DRL for 5G network slicing optimization. The numerical results demonstrate that the proposed approach achieves superior performance compared with baseline solutions.