YOLO-based Semantic Communication with Generative AI-aided Resource Allocation for Digital Twins Construction

TL;DR

This paper introduces a YOLO-based semantic communication framework with AI-driven resource allocation for digital twins, reducing data transmission costs and improving important information delivery in virtual-physical system synchronization.

Contribution

It proposes a novel YOLOv7-X based semantic extraction method combined with confidence and AI-generated resource allocation schemes, including a diffusion model for optimal data transmission in digital twins.

Findings

Significant reduction in data transmission costs.

Enhanced detection capability with ELAN-H and SimAM modules.

Improved transmission quality of critical semantic information.

Abstract

Digital Twins play a crucial role in bridging the physical and virtual worlds. Given the dynamic and evolving characteristics of the physical world, a huge volume of data transmission and exchange is necessary to attain synchronized updates in the virtual world. In this paper, we propose a semantic communication framework based on You Only Look Once (YOLO) to construct a virtual apple orchard with the aim of mitigating the costs associated with data transmission. Specifically, we first employ the YOLOv7-X object detector to extract semantic information from captured images of edge devices, thereby reducing the volume of transmitted data and saving transmission costs. Afterwards, we quantify the importance of each semantic information by the confidence generated through the object detector. Based on this, we propose two resource allocation schemes, i.e., the confidence-based scheme and the artificial intelligence-generated scheme, aimed at enhancing the transmission quality of important semantic information. The proposed diffusion model generates an optimal allocation scheme that outperforms both the average allocation scheme and the confidence-based allocation scheme. Moreover, to obtain semantic information more effectively, we enhance the detection capability of the YOLOv7-X object detector by introducing new Efficient Layer Aggregation Network-HorNet (ELAN-H) and SimAM attention modules, while reducing the model parameters and computational complexity, making it easier to run on edge devices with limited performance. The numerical results indicate that our proposed semantic communication framework and resource allocation schemes significantly reduce transmission costs while enhancing the transmission quality of important information in communication services.