用于In-X 6G子网络的动态干扰预测
Dynamic Interference Prediction for In-X 6G Sub-networks
AI总结：
本文提出了一种基于3GPP信道模型的动态干扰预测方法，通过离散时间动态状态空间模型和扩展卡尔曼滤波器，利用CQI报告预测干扰功率值，从而在6G子网络中实现更高效的干扰管理。
摘要：
第六代（6G）工业子网络（SNs）面临在0.1-1毫秒和99.999-至99.99999百分位数范围内满足极端时延和可靠性要求的挑战。干扰管理（IM）在解决这些要求中起着关键作用，尤其是在超密集SN环境中，由于信道特性、移动性和资源限制导致的快速变化的干扰。通常，IM可以通过资源分配和准确的链路自适应（LA）来实现。在本工作中，我们专注于后者，首先使用空间一致的3GPP信道模型对SN设备的干扰进行建模。随后，我们提出了一种离散时间动态状态空间模型（DSSM），在SN接入点（AP）处，将干扰功率值（IPVs）建模为隐变量，结合底层建模误差以及传输/协议延迟。然后提出了必要的近似方法，以简化DSSM并高效地使用扩展卡尔曼滤波器（EKF）进行干扰预测。与基线方法不同，我们提出的方法仅基于SN AP在每个传输时间间隔（TTI）可用的信道质量指示器（CQI）报告来预测IPVs。数值结果表明，我们提出的方法明显优于传统基线方法。此外，我们还展示了尽管使用有限的信息进行预测，我们的方法在与现成的监督学习基线相比时仍能保持相当的性能。
英文摘要：
The sixth generation (6G) industrial Sub-networks (SNs) face several challenges in meeting extreme latency and reliability requirements in the order of 0.1-1 ms and 99.999 -to-99.99999 percentile, respectively. Interference management (IM) plays an integral role in addressing these requirements, especially in ultra-dense SN environments with rapidly varying interference induced by channel characteristics, mobility, and resource limitations. In general, IM can be achieved using resource allocation and \textit{accurate} Link adaptation (LA). In this work, we focus on the latter, where we first model interference at SN devices using the spatially consistent 3GPP channel model. Following this, we present a discrete-time dynamic state space model (DSSM) at a SN access point (AP), where interference power values (IPVs) are modeled as latent variables incorporating underlying modeling errors as well as transmission/protocol delays. Necessary approximations are then presented to simplify the DSSM and to efficiently employ the extended Kalman filter (EKF) for interference prediction. Unlike baseline methods, our proposed approach predicts IPVs solely based on the channel quality indicator (CQI) reports available at the SN AP at every transmission time interval (TTI). Numerical results demonstrate that our proposed approach clearly outperforms the conventional baseline. Furthermore, we also show that despite predicting with limited information, our proposed approach consistently achieves a comparable performance w.r.t the off-the-shelf supervised learning based baseline.
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