Xudong Wang;Hongyang Du;Lei Feng;Fanqin Zhou;Wenjing Li
{"title":"工业物联网系统中支持noma的URLLC传输的有效吞吐量最大化:一种基于生成ai的方法","authors":"Xudong Wang;Hongyang Du;Lei Feng;Fanqin Zhou;Wenjing Li","doi":"10.1109/JIOT.2025.3533570","DOIUrl":null,"url":null,"abstract":"The development of B5G and 6G technologies has led to an explosive growth in device connectivity density in Industrial Internet of Things (IIoT) systems. However, the limited spectrum resources in industrial wireless networks pose significant challenges for large-scale access and communication rates, especially for factory automation applications that are sensitive to control stability and latency. In this article, we investigate an uplink nonorthogonal multiple access (NOMA) transmission for ultrareliable and low-latency communication services in IIoT systems, where sensors in NOMA clusters transmit collected data to the base station to meet the high communication rate and control stability requirements of controlled devices. The dynamic control convergence constraint is theoretically transformed into an optimal control condition in each communication round based on the decoding error probability. Additionally, we formulate an optimization problem to maximize the effective throughput of the considered system in the finite blocklength regime by jointly optimizing blocklength allocation, power allocation, and decoding error probability. To solve this mixed integer nonlinear programming problem, we decompose it into two subproblems and propose an efficient optimization framework based on generative AI. Specifically, we apply successive convex approximation to solve the blocklength allocation subproblem, and use a diffusion model to address the joint power control and decoding error probability subproblem. Finally, extensive simulation results demonstrate the effectiveness of this approach.","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":"12 10","pages":"13327-13339"},"PeriodicalIF":8.9000,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Effective Throughput Maximization for NOMA-Enabled URLLC Transmission in Industrial IoT Systems: A Generative AI-Based Approach\",\"authors\":\"Xudong Wang;Hongyang Du;Lei Feng;Fanqin Zhou;Wenjing Li\",\"doi\":\"10.1109/JIOT.2025.3533570\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The development of B5G and 6G technologies has led to an explosive growth in device connectivity density in Industrial Internet of Things (IIoT) systems. However, the limited spectrum resources in industrial wireless networks pose significant challenges for large-scale access and communication rates, especially for factory automation applications that are sensitive to control stability and latency. In this article, we investigate an uplink nonorthogonal multiple access (NOMA) transmission for ultrareliable and low-latency communication services in IIoT systems, where sensors in NOMA clusters transmit collected data to the base station to meet the high communication rate and control stability requirements of controlled devices. The dynamic control convergence constraint is theoretically transformed into an optimal control condition in each communication round based on the decoding error probability. Additionally, we formulate an optimization problem to maximize the effective throughput of the considered system in the finite blocklength regime by jointly optimizing blocklength allocation, power allocation, and decoding error probability. To solve this mixed integer nonlinear programming problem, we decompose it into two subproblems and propose an efficient optimization framework based on generative AI. Specifically, we apply successive convex approximation to solve the blocklength allocation subproblem, and use a diffusion model to address the joint power control and decoding error probability subproblem. Finally, extensive simulation results demonstrate the effectiveness of this approach.\",\"PeriodicalId\":54347,\"journal\":{\"name\":\"IEEE Internet of Things Journal\",\"volume\":\"12 10\",\"pages\":\"13327-13339\"},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2025-01-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Internet of Things Journal\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10852212/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Internet of Things Journal","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10852212/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Effective Throughput Maximization for NOMA-Enabled URLLC Transmission in Industrial IoT Systems: A Generative AI-Based Approach
The development of B5G and 6G technologies has led to an explosive growth in device connectivity density in Industrial Internet of Things (IIoT) systems. However, the limited spectrum resources in industrial wireless networks pose significant challenges for large-scale access and communication rates, especially for factory automation applications that are sensitive to control stability and latency. In this article, we investigate an uplink nonorthogonal multiple access (NOMA) transmission for ultrareliable and low-latency communication services in IIoT systems, where sensors in NOMA clusters transmit collected data to the base station to meet the high communication rate and control stability requirements of controlled devices. The dynamic control convergence constraint is theoretically transformed into an optimal control condition in each communication round based on the decoding error probability. Additionally, we formulate an optimization problem to maximize the effective throughput of the considered system in the finite blocklength regime by jointly optimizing blocklength allocation, power allocation, and decoding error probability. To solve this mixed integer nonlinear programming problem, we decompose it into two subproblems and propose an efficient optimization framework based on generative AI. Specifically, we apply successive convex approximation to solve the blocklength allocation subproblem, and use a diffusion model to address the joint power control and decoding error probability subproblem. Finally, extensive simulation results demonstrate the effectiveness of this approach.
期刊介绍:
The EEE Internet of Things (IoT) Journal publishes articles and review articles covering various aspects of IoT, including IoT system architecture, IoT enabling technologies, IoT communication and networking protocols such as network coding, and IoT services and applications. Topics encompass IoT's impacts on sensor technologies, big data management, and future internet design for applications like smart cities and smart homes. Fields of interest include IoT architecture such as things-centric, data-centric, service-oriented IoT architecture; IoT enabling technologies and systematic integration such as sensor technologies, big sensor data management, and future Internet design for IoT; IoT services, applications, and test-beds such as IoT service middleware, IoT application programming interface (API), IoT application design, and IoT trials/experiments; IoT standardization activities and technology development in different standard development organizations (SDO) such as IEEE, IETF, ITU, 3GPP, ETSI, etc.