Radio Resource Scheduling with Deep Pointer Networks and Reinforcement Learning

• Author(s): Al-Tam, Faroq ; Mazayev, Andriy ; Correia, Noélia ; Rodriguez, J.
• Date: 2020
• Persistent ID: http://hdl.handle.net/10400.1/16612
• Origin: Sapientia - Universidade do Algarve
• Subject(s): 5G; Deep reinforcement learning; Radio resource management; Radio resource scheduling; Pointer network; Actor-critic
• Document Type: Conference object
• Language: English
• Contributor(s): Sapientia

Description

This article presents an artificial intelligence (AI) adaptable solution to handle the radio resource scheduling (RRS) task in 5G networks. RRS is one of the core tasks in radio resource management (RRM) and aims to efficiently allocate frequency domain resources to users. The proposed solution is an advantage pointer critic (APC) deep reinforcement learning (DRL) agent. It is built with a deep pointer network architecture and trained by the policy gradient algorithm. The proposed agent is deployed in a system level simulator and the experimental results demonstrate its adaptability to network dynamics and efficiency when compared to baseline algorithms.