Indirect Estimation of SINR via SSB and CSI-RS RSRP in 5G NR

2026-09-03

AI Summary

本文提出一种基于SSB和CSI-RS RSRP测量的数据驱动方法，通过监督学习预测5G NR中的平均下行SINR，以解决UE性能预测难题。

Abstract

Predicting user equipment (UE) performance is essential for proactive network control, resource management, and digital twin sandboxes. However, the inherent flexibility and complexity of beam-based 5G new radio (NR) networks make accurate performance forecasting highly challenging. This paper proposes a data-driven approach to predict the average downlink signal-to-interference-plus-noise ratio (SINR) relying exclusively on standardized reference-signal measurements, namely synchronization signal block (SSB) and channel state information-reference signal (CSI-RS) reference signal received power (RSRP). We formulate this prediction as a supervised learning problem and evaluate various input feature representations using a third generation partnership project (3GPP)-compliant synthetic dataset. Our analysis reveals that filtering measurements based on active CSI-RS beams significantly enhances prediction accuracy while reducing input dimensionality. This activity-aware strategy demonstrates the strong viability of machine learning models for proactive network optimization.

Problem

• 5G NR
• SINR
• SSB
• CSI-RS
• UE performance

Innovation

• data-driven approach
• SINR prediction
• SSB and CSI-RS RSRP
• supervised learning
• activity-aware strategy