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arXiv · 2609.03488

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

Leonardo Spampinato·Mahamadou Togola·Matteo Bernabè·Azim Akhtarshenas·Lorenzo Mario Amorosa·David López-Pérez
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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.

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Leonardo Spampinato, Mahamadou Togola, Matteo Bernabè, Azim Akhtarshenas, Lorenzo Mario Amorosa, David López-Pérez. 2026-09-03. Indirect Estimation of SINR via SSB and CSI-RS RSRP in 5G NR. https://arxiv.org/abs/2609.03488

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