Extreme Value Theory-Based Predictive Interference Management for 6G Subnetworks with Transformer

In-X subnetworks (SNs) encounter significant challenges in achieving hyper-reliable low-latency communication (HRLLC), particularly in hyper-dense deployment scenarios. These challenges stem from rapid and dynamic variations in interference caused by mobility, dynamic channel statistics, and varying traffic patterns. Predictive interference management plays a crucial role in meeting these extreme requirements, by enabling proactive and reliable resource allocation to prevent performance degradation. To address these challenges, we propose a probabilistic interference prediction technique using an inverted quantile transformer (iQTransformer) to learn interference dynamics effectively and capture tail statistics. It predicts interference for multiple sensor-actuator (SA) pairs in the SN with minimal disparity by leveraging their own interference dynamics alongside those of other active SA pairs. We integrate extreme value theory (EVT) with the iQTransformer to handle rare and extreme interference events. Results demonstrate that our proposed method outperforms baseline approaches in terms of prediction accuracy, achieving near-optimal target reliability.