Radio Resource Scheduling with Deep Pointer Networks and Reinforcement Learning

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.European Regional Development Fund (FEDER), through the Competitiveness and Internationalization Operational Programme (COMPETE 2020)Regional Operational Program of the Algarve (2020)Fundacao para a ciencia e TecnologiaPortuguese Foundation for Science and TechnologyEuropean Commissioni-Five: Extensao do acesso de espectro dinamico para radio 5G [POCI-01-0145-FEDER-030500]Fundacao para a ciencia e Tecnologia, Portugal, within CEOT (Center for Electronic, Optoelectronic and Telecommunications) [UID/MULTI/00631/2020]