Digital-Twin-Assisted Resource Allocation for Network Slicing in Industry 4.0 and Beyond Using Distributed Deep Reinforcement Learning

Abstract

Personalization is one of the primary emerging trends in Industry 4.0 and Beyond. Highly personalized services will present a significant challenge to the existing algorithms for network slicing (NS) and resource allocation, leading to issues, such as nonequilibratory resource allocation, in which some services are sacrificed for the maximum total reward of the algorithm, excessive cost, and slow algorithm convergence. A digital twin network (DTN) is offered as a novel solution to the challenges listed above. By integrating the DTN and IIoT NS, we propose a DTN-assisted industry Internet of Things NS (DTN-IIoT NS) architecture for personalized IIoT services in Industry 4.0 and Beyond. The DTN-IIoT NS architecture consists of three layers, three modules, and two closed loops. On the basis of the aforementioned architecture, we focus on the resource allocation process in DTN-IIoT NS, model the DT-assisted resource allocation for highly personalized IIoT services, propose the service equilibrium rate, and formulate the optimization problem aiming at maximizing the equilibrium rate weighted net profit of network providers. Then, we propose a dual-channel weighted (DCW) Critic network for service equilibrium in DTN-IIoT NS resource allocation and the matching Improved prioritized experience replay (PER) to enhance convergent speed. In addition, we present a distributed DT-assisted DCW-PER multiagent deep deterministic policy gradient (PER-DCW MADDPG) algorithm for the resource allocation process in DTN-IIoT NS. Simulation results indicate that the PER-DCW MADDPG algorithm can produce a better service equilibrium and accelerate the convergence speed of the algorithm.