Intent-Aware DRL-Based Uplink Dynamic Scheduler for 5G-NR
Salwa Mostafa Mateus P. Mota Alvaro Valcarce Mehdi Bennis
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, a graph-based
reduction scheme is proposed to reduce the state and action space of the RL
framework to allow fast convergence and a better learning strategy. Simulation
results demonstrate the effectiveness of the proposed intelligent scheduler in
guaranteeing the expressed intent of IIoT UEs compared to several traditional
scheduling schemes, such as round-robin, semi-static, and heuristic approaches.
The proposed scheduler also outperforms the contention-free and
contention-based schemes in maximizing the number of successfully computed
tasks.