Deep Q-Network for Optimizing NOMA-Aided Resource Allocation in Smart Factories with URLLC Constraints

TL;DR

This paper introduces a Deep Q-Network based algorithm for resource allocation in NOMA-enabled smart factories, effectively balancing throughput and URLLC latency constraints for diverse industrial devices.

Contribution

The paper proposes a novel DQN-based resource allocation method that dynamically manages sub-channels and power levels, incorporating a tunable parameter to optimize performance trade-offs.

Findings

Robots achieve higher throughput with the algorithm.

Sensors and controllers meet URLLC latency requirements.

The method effectively balances throughput and latency in industrial settings.

Abstract

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.