Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks

TL;DR

This paper introduces a deep reinforcement learning-based scheduling scheme for cellular networks that enhances performance, scalability, and robustness, demonstrated through simulations and field tests.

Contribution

It presents a scalable neural network design and a virtual environment training framework for DRL-based scheduling in cellular networks, enabling practical deployment.

Findings

DRL-based scheduling outperforms traditional methods in simulations.

The scalable neural network handles varying user numbers without retraining.

Training in virtual environments improves robustness and deployment readiness.

Abstract

To improve the system performance towards the Shannon limit, advanced radio resource management mechanisms play a fundamental role. In particular, scheduling should receive much attention, because it allocates radio resources among different users in terms of their channel conditions and QoS requirements. The difficulties of scheduling algorithms are the tradeoffs need to be made among multiple objectives, such as throughput, fairness and packet drop rate. We propose a smart scheduling scheme based on deep reinforcement learning (DRL). We not only verify the performance gain achieved, but also provide implementation-friend designs, i.e., a scalable neural network design for the agent and a virtual environment training framework. With the scalable neural network design, the DRL agent can easily handle the cases when the number of active users is time-varying without the need to redesign and retrain the DRL agent. Training the DRL agent in a virtual environment offline first and using it as the initial version in the practical usage helps to prevent the system from suffering from performance and robustness degradation due to the time-consuming training. Through both simulations and field tests, we show that the DRL-based smart scheduling outperforms the conventional scheduling method and can be adopted in practical systems.