Goal-Oriented Interference Coordination in 6G In-Factory Subnetworks

IF 17.2
Daniel Abode;Pedro Maia de Sant Ana;Ramoni Adeogun;Alexander Artemenko;Gilberto Berardinelli
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Abstract

Subnetworks are expected to enhance wireless pervasiveness for critical applications such as wireless control of plants, however, they are interference-limited due to their extreme density. This paper proposes a goal-oriented joint power and multiple sub-bands allocation policy for interference coordination in 6G in-factory subnetworks. Current methods for interference coordination in subnetworks only focus on optimizing communication metrics, such as the block error rate, without considering the goal of the controlled plants. This oversight often leads to inefficient allocation of the limited radio resources. To address this, we devise a novel decentralized inter-subnetwork interference coordination policy optimized using a Bayesian framework to ensure the long-term stability of the subnetwork-controlled plants. Our results show that the proposed decentralized method can support more than twice the density of subnetwork-controlled plants compared to centralized schemes that aim to minimize the block error rate while reducing execution complexity significantly.
6G厂内子网面向目标的干扰协调
子网络有望增强关键应用(如无线控制植物)的无线普及,然而,由于它们的极端密度,它们受到干扰限制。针对6G工厂子网干扰协调问题,提出了一种目标导向的联合功率多频段分配策略。目前的子网络干扰协调方法只关注优化通信指标,如分组错误率,而没有考虑被控对象的目标。这种疏忽往往导致有限的无线电资源分配效率低下。为了解决这个问题,我们设计了一种新的分散的子网间干扰协调策略,该策略使用贝叶斯框架进行优化,以确保子网控制工厂的长期稳定性。我们的研究结果表明,与旨在最小化块错误率同时显着降低执行复杂性的集中式方案相比,所提出的分散方法可以支持子网控制工厂密度的两倍以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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