Joint Model and Data-Driven Two-Stage Uplink Interference Prediction in URLLC Scenarios

In the context of Ultra-Reliable Low Latency Communication (URLLC) scenarios, 5G incorporates numerous enhancements, with link adaptation (LA) being one of them. In the pursuit of reliability, a measurement-prediction-decision approach can be considered to enhance the accuracy of Modulation and Coding Scheme (MCS) decisions during LA, specifically by forecasting interference. In this paper, a two-stage uplink interference prediction algorithm is proposed. In the first stage, complex uplink interference values are decomposed to extract inherent patterns. In the second stage, leveraging the prior knowledge provided by the first stage, which enhances the algorithm's robustness and accuracy, inference is made. The experimental results demonstrate that the proposed interference prediction algorithm not only exhibits a significant improvement in accuracy but also contributes to a substantial enhancement in the performance of the communication system.