Sentinel: Vision-Based Signaling-Free SNR Prediction for Proactive 5G Resource Management

Abstract

Reliable and efficient radio resource management in 5G systems critically depends on accurate Channel State Information (CSI) availability at the base stations. Traditionally, base stations perform scheduling, resource allocation, and link adaptation using the Channel Quality Indicator (CQI), either computed directly for uplink or obtained via CSI feedback reports for downlink. Both uplink and downlink procedures rely on frequent pilot and feedback transmissions, introducing significant overhead that challenges scalability and ultra-reliable communication demands. In this work, we introduce Sentinel, a vision-based machine learning system that leverages grayscale image sequences from an indoor environment to predict the SNR between user equipment and base station with a foresight window of 200 ms. Sentinel’s SNR prediction enables flexible CQI acquisition, allowing different SNR-to-CQI mappings without modifying the system, and eliminates the need for CQI-related pilot or feedback signaling. The proposed system is evaluated in a dynamic multi-user scenario comprising three heterogeneous 5QI service profiles across 40 users. Sentinel demonstrates superior CQI prediction performance, achieving substantial to near-perfect agreement with true CQI labels, as measured by the quadratic weighted kappa, and outperforming benchmark foresight-based CQI prediction models in both CQI classification and resource management effectiveness. Proactive resource management evaluations show that Sentinel meets the strict reliability targets of mission-critical 5QI services, achieving packet error rates below $10^{-4}$ , and approaching $10^{-5}$ when integrated with signaling. Furthermore, Sentinel reduces total radio resource usage by up to 24% in the 40-user scenario by eliminating CQI-related signaling overhead.