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

IF 8.9 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xudong Wang;Hongyang Du;Lei Feng;Fanqin Zhou;Wenjing Li
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引用次数: 0

摘要

B5G和6G技术的发展导致工业物联网(IIoT)系统中设备连接密度的爆炸式增长。然而,工业无线网络中有限的频谱资源对大规模访问和通信速率构成了重大挑战,特别是对于对控制稳定性和延迟敏感的工厂自动化应用。在本文中,我们研究了用于工业物联网系统中超可靠和低延迟通信服务的上行非正交多址(NOMA)传输,其中NOMA集群中的传感器将收集的数据传输到基站,以满足被控设备的高通信速率和控制稳定性要求。从理论上将动态控制收敛约束转化为每轮通信中基于译码错误概率的最优控制条件。此外,我们制定了一个优化问题,通过联合优化块长度分配,功率分配和译码错误概率来最大化所考虑的系统在有限块长度制度下的有效吞吐量。为了解决这一混合整数非线性规划问题,我们将其分解为两个子问题,并提出了一种基于生成式人工智能的高效优化框架。具体地说,我们应用连续凸逼近来解决块长度分配子问题,并使用扩散模型来解决联合功率控制和解码错误概率子问题。最后,大量的仿真结果验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
IEEE Internet of Things Journal
IEEE Internet of Things Journal Computer Science-Information Systems
CiteScore
17.60
自引率
13.20%
发文量
1982
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.
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