Deep Reinforcement Learning Based Resource Allocation for Network Slicing With Massive MIMO

Abstract

Network slicing is a critical technology for fifth-generation (5G) networks, owing to its merits in meeting the diversified requirements of users. Effective resource allocation for network slicing in Radio Access Networks (RAN) is still challenging owing to dynamic service requirements. Therein, automatic resource allocation based on environmental changes is of significant importance for network slicing. In this study, we used deep reinforcement learning (DRL) to allocate resources for network slicing in a RAN with the aid of massive multiple-input multiple-output (MIMO). The DRL agent interacts with the environment to execute autonomous resource allocation. We considered a two-level scheduling framework that aims to maximize the quality of experience (QoE) and spectrum efficiency (SE) of slices. The proposed algorithm can find a near-optimal solution. We used the standard DRL advantage actor-critic (A2C) algorithm to implement upper-level inter-slice bandwidth resource allocation that considers service traffic dynamics in a large timescale. Lower-level scheduling is a mixed-integer stochastic optimization problem with several constraints. We combined the proportional fair scheduling algorithm and the water filling algorithm to perform resource block (RB) and power allocation in a small timescale. The results show that the QoE and SE of all slices using the A2C algorithm achieved a significant performance improvement over the other algorithms. The efficiency of the proposed method was supported by the simulation results.