Neural CRC Prediction for 5G NR URLLC

Abstract

We propose a neural cyclic redundancy check (CRC) predictor for the 5G New Radio (5G NR) physical uplink shared channel (PUSCH) that enables early link-adaptation decisions for Ultra-Reliable Low-Latency Communications (URLLC). The predictor combines a lightweight convolutional neural network (CNN) with a fixed front-end that extracts multi-scale time-frequency energy features from the received signal and least-squares channel estimates. We investigate two complementary front-end realizations - a wavelet scattering front-end built from fixed Gabor filters, and an FFT-based scattering front-end that applies bandpass masks in the frequency domain with geometric scale spacing. Drawing on neural-receiver design principles, the predictor estimates the post-decoding CRC outcome directly from the received resource grid, bypassing the conventional equalization and decoding chain. We further introduce the modulation and coding scheme (MCS) index as an auxiliary conditioning input that adapts the decision boundary to the operating code rate. Experiments on a multi-MCS 5G NR PUSCH dataset show that the hybrid scattering predictors substantially outperform a pure CNN baseline, with MCS conditioning further improving reliability across varying channel conditions. Both front-ends are compatible with GPU-accelerated inference and are lightweight enough to be deployed within a real-time baseband pipeline. We further demonstrate an evidential deep learning extension that quantifies epistemic uncertainty in a single forward pass using a conservative decision rule.