文献类型：期刊文献

英文题名：Dynamic Resource Allocation and Energy Consumption Optimization Strategy in Edge Intelligent Networks Based on GANs

作者：Xiao, Kaile[1]; Chen, Guang[2]; Sun, ZhiYuan[1]; Zhao, Haiyan[1]

第一作者：Xiao, Kaile

机构：[1] Beijing Union University, School of Applied Science and Technology, Beijing, China; [2] Software Development Center China CITIC Bank, Beijing, China

第一机构：北京联合大学应用科技学院

年份：2025

起止页码：584-587

外文期刊名：2025 5th International Symposium on Computer Technology and Information Science, ISCTIS 2025

收录：EI(收录号：20253118896111)

语种：英文

外文关键词：Intelligent networks - Optimization - Resource allocation - Signal processing

摘要：With the rapid development of the Internet of Things and artificial intelligence technology, edge intelligent networks have been widely applied in many fields, and their resource allocation and energy consumption optimization have become key issues. This article aims to meet energy constraints and minimize latency by introducing Generative Adversarial Networks (GANs). The generator generates task offloading and resource allocation schemes based on the network state, and the discriminator judges the quality of the schemes. Through adversarial training of the two, continuous optimization of resource allocation schemes is achieved. The experimental results show that compared to traditional algorithms, the algorithm proposed in this paper has significant advantages in resource utilization and system energy consumption, and can effectively improve the resource allocation performance of edge intelligent networks. ? 2025 IEEE.