This paper presents a Deep Q-Network (DQN)- based algorithm for NOMA-aided resource allocation in smart factories, addressing the stringent requirements of Ultra-Reliable Low-Latency Communication (URLLC). The proposed algorithm dynamically allocates sub-channels and optimizes power levels to maximize throughput while meeting strict latency constraints. By incorporating a tunable parameter {\lambda}, the algorithm balances the trade-off between throughput and latency, making it suitable for various devices, including robots, sensors, and controllers, each with distinct communication needs. Simulation results show that robots achieve higher throughput, while sensors and controllers meet the low-latency requirements of URLLC, ensuring reliable communication for real-time industrial applications.
@article{arxiv.2511.00765,
title = {Deep Q-Network for Optimizing NOMA-Aided Resource Allocation in Smart Factories with URLLC Constraints},
author = {Shi Gengtian and Jiang Liu and Shigeru Shimamoto},
journal= {arXiv preprint arXiv:2511.00765},
year = {2025}
}
Comments
Accepted for presentation at the IEEE Wireless Communications and Networking Conference (WCNC) 2025. This is the preprint version of the paper