Intent-Aware DRL-Based NOMA Uplink Dynamic Scheduler for IIoT
This work addresses resource allocation challenges for IIoT systems, offering an incremental improvement over existing scheduling schemes.
The paper tackles the problem of scheduling communication resources for Industrial Internet of Things devices with varying quality-of-service demands and random traffic, proposing a deep reinforcement learning-based scheduler that outperforms traditional methods in guaranteeing intent and maximizing successfully computed tasks.
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.