Deep Reinforcement Learning for Resource Allocation in 5G Communications

Abstract

The rapid growth of data traffic has pushed the mobile telecommunication industry towards the adoption of fifth generation (5G) communications. Cloud radio access network (CRAN), one of the 5G key enabler, facilitates fine-grained management of network resources by separating the remote radio head (RRH) from the baseband unit (BBU) via a highspeed front-haul link. Classical resource allocation (RA) schemes rely on numerical techniques to optimize various performance metrics. Most of these works can be defined as instantaneous since the optimization decisions are derived from the current network state without considering past network states. While utility theory can incorporate long-term optimization effect into these optimization actions, the growing heterogeneity and complexity of network environments has rendered the RA issue intractable. One prospective candidate is reinforcement learning (RL), a dynamic programming framework which solves the RA problems optimally over varying network states. Still, such method cannot handle the highly dimensional state-action spaces in the context of CRAN problems. Driven by the success of machine learning, researchers begin to explore the potential of deep reinforcement learning (DRL) to address the RA problems. In this work, an overview of the major existing DRL approaches in CRAN is presented. We conclude this article by identifying current technical hurdles and potential future research directions.