AI RESEARCH
ML-Based Real-Time Downlink Performance Prediction in Standalone 5G NR Using Smartphones
arXiv CS.LG
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ArXi:2604.09632v1 Announce Type: cross We propose a machine learning (ML)-based framework for downlink performance prediction in 5G networks using real-time measurements from commercial off-the-shelf (COTS) user equipment (UE). Our experimental platform integrates the srsRAN 5G New Radio (NR) stack deployed on a Dell desktop serving as the 5G next generation nodeB (gNB), operating at 3.4 GHz.