利用分布式大规模多输入多输出(MIMO)调度工业控制流量,实现动态 RAN 分片

Future Internet Pub Date : 2024-02-23 DOI:10.3390/fi16030071
Emma Fitzgerald, Michal Pióro
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引用次数: 0

摘要

工业 4.0 注重灵活性和可定制性,正在推动未来智能工厂的无线通信方向,特别是大规模多输入多输出(MIMO)及其未来演进的大型智能表面(LIS),与以往的技术相比,可提供更可靠的信道质量。与此同时,5G 及更先进系统中的网络切片技术可以更轻松地管理不同类别的用户和流量,并为提供服务质量奠定更好的基础,特别是对于工业控制等要求苛刻的用例。在以前的工作中,我们提出了在 LIS 和大规模 MIMO 系统中调度工业控制流量的解决方案。现在,我们考虑在无线接入网络中进行动态分片的情况,在这种情况下,我们不仅需要满足工业控制流量对延迟和可靠性的严格要求,还要最大限度地减少为控制流量服务的网络分片所占用的无线资源,确保资源可用于优先级较低的流量分片。在本文中,我们为动态网络切片的无线电资源使用最小化提供了混合整数编程优化公式。我们在数值实验中测试了我们的公式,实验中使用了不同的流量剖面和节点数量,最多可达 32 个节点。对于所有测试过的问题实例,我们都能在 1 秒内计算出最佳时间表,这使我们的方法在实际部署场景中的应用变得可行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO
Industry 4.0, with its focus on flexibility and customizability, is pushing in the direction of wireless communication in future smart factories, in particular, massive multiple-input-multiple-output (MIMO) and its future evolution of large intelligent surfaces (LIS), which provide more reliable channel quality than previous technologies. At the same time, network slicing in 5G and beyond systems provides easier management of different categories of users and traffic, and a better basis for providing quality of service, especially for demanding use cases such as industrial control. In previous works, we have presented solutions for scheduling industrial control traffic in LIS and massive MIMO systems. We now consider the case of dynamic slicing in the radio access network, where we need to not only meet the stringent latency and reliability requirements of industrial control traffic, but also minimize the radio resources occupied by the network slice serving the control traffic, ensuring resources are available for lower-priority traffic slices. In this paper, we provide mixed-integer programming optimization formulations for radio resource usage minimization for dynamic network slicing. We tested our formulations in numerical experiments with varying traffic profiles and numbers of nodes, up to a maximum of 32 nodes. For all problem instances tested, we were able to calculate an optimal schedule within 1 s, making our approach feasible for use in real deployment scenarios.
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