arXiv:2611.07685v1 Announce Type: new Abstract: The physical random access channel (PRACH) enables initial access between user equipment and the base station, making reliable preamble detection essential for cellular networks. Growing device density in 5G and beyond-5G networks, massive machine-type communication, and the shift towards machine-learning-driven radio interfaces challenge conventional analytical detectors, particularly in complex radio environments and at low signal-to-noise ratios. This article formulates preamble detection as a 64-class classification problem and introduces two model-agnostic contributions: a robust adaptive threshold implementing the constant false alarm rate (CFAR) principle in the classifier decision-score domain (MAD-CFAR), and a hybrid receiver combining a learned detector with a conventional power-delay-profile CFAR detector through a logical OR rule. The learned branch is evaluated using two models trained on the same GPU-generated dataset: a support vector machine (SVM) with principal component analysis and a lightweight one-dimensional convolutional neural network (CNN). On a held-out test set, the SVM achieves 98.58% accuracy (macro-AUC 0.9991), while the CNN reaches 99.29% accuracy (macro-AUC 1.0000) at lower inference cost and memory footprint. MAD-CFAR reduces the learned branch's false alarm rate to near zero on the noise calibration campaign. The OR hybrid retains the analytical detector's low, standard-compliant false alarm rate while extending detection capability. Across six propagation channels and 258 conformance-style tests, the SVM-based hybrid improves low-SNR sensitivity by several decibels without increasing missed detections relative to the analytical detector. Under the same false alarm constraint, the CNN is competitive in some channels but currently trails the SVM in high-delay-spread multipath conditions.