Intent-Aware DRL-Based Uplink Dynamic Scheduler for 5G-NR

arXiv:2403.18364v1 Announce Type: cross
Abstract: We investigate the problem of supporting Industrial Internet of Things user equipment (IIoT UEs) with intent (i.e., requested quality of service (QoS)) and random traffic arrival. A deep reinforcement learning (DRL) based centralized dynamic scheduler for time-frequency resources is proposed to learn how to schedule the available communication resources among the IIoT UEs. The proposed scheduler leverages an RL framework to adapt to the dynamic changes in the wireless communication system and traffic arrivals. Moreover, …

abstract arxiv communication cs.ai cs.it cs.lg dynamic equipment iiot industrial industrial internet of things internet internet of things learn math.it quality random reinforcement reinforcement learning resources service traffic type