5G Resource Scheduling for Low-latency Communication: A Reinforcement Learning Approach
Qian Huang, Michel Kadoch
Abstract
The emergence of various applications is driving the continuous development of 5G mobile networks as infrastructure. To better support real-time wireless services, the low-latency communication is the current research focus. However, the existing radio resource scheduling methods cannot guarantee the strict delay constraint of the low-latency communication. Therefore, a reinforcement learning (RL) approach of radio spectrum resource scheduling strategy is proposed, which can guarantee the low-latency constraint when spectrum resources are insufficient. The Q-learning algorithm is used to approximate the optimal goal of RL. To speed up the learning, the deep neural network (DNN) is used to train the learning parameters. Simulation results show that the strategy converges quickly and has satisfactory results for mobile networks with a high load of spectrum resources.