Synergistic Integration of Deep Reinforcement Learning and 5G Network Slicing for Optimized Resource Allocation in Industrial IoT Frameworks

Abstract

The rapid evolution of the Fifth Generation (5G) wireless standard has introduced transformative capabilities
for Industrial Internet of Things (IIoT) ecosystems, characterized by diverse Quality of Service (QoS) requirements acros s
massive Machine-Type Communications (mMTC) and Ultra-Reliable Low-Latency Communications (URLLC). Central to
this evolution is network slicing, which permits the creation of virtualized logical networks over a shared physical
infrastructure. However, the dynamic and stochastic nature of IIoT traffic patterns renders traditional heuristic -based
resource allocation methods inefficient. This paper proposes an AI-driven framework leveraging Deep Reinforcement
Learning (DRL) to optimize dynamic resource provisioning within 5G core architectures.
The methodology employs an Asynchronous Advantage Actor-Critic (A3C) algorithm to manage radio resource blocks and
backhaul bandwidth, aiming to minimize end-to-end latency while maximizing spectral efficiency. Computer engineering
simulations were conducted using a discrete-event network simulator integrated with a high-performance Python-based AI
backend. Quantitative results demonstrate that the proposed DRL-based approach achieves a 28.5% reduction in packet
drop rates and a 19.4% improvement in overall throughput compared to conventional Proportional Fair (PF) scheduling
algorithms. Furthermore, the model demonstrates rapid convergence rates, maintaining stability under high -mobility
scenarios exceeding 100 km/h. The study concludes that the integration of autonomous AI agents within the 5G Radio
Access Network (RAN) is vital for achieving the sub-millisecond latency targets required for mission-critical industrial
automation, providing a scalable blueprint for future 6G architectural designs