arXiv:2611.13490v1 Announce Type: new Abstract: We design and evaluate a site-specific neural receiver for the 5G New Radio (NR) uplink. A corridor digital twin built from a radar scan and refined manually provides 28,671 ray-traced channel records for training an eight-round convolutional graph neural network with pilot-aware inputs and auxiliary channel supervision. The GPU on NVIDIA DGX Spark evaluates time--frequency convolutions in parallel across the received grid to accelerate inference. The receiver uses the standard NR uplink waveform and reference signals and feeds soft bits to the existing transport-block decoder. In over-the-air tests with NI USRP B210 radios under common radio settings, the site-trained receiver achieves the highest mean uplink rate at all five positions. At a non-line-of-sight corner, it reaches 16.2 Mb/s, compared with 4.1 Mb/s for the conventional receiver and 2.4 Mb/s for the 3GPP-trained neural receiver. These results demonstrate the feasibility of integrating neural reception into the 5G NR physical layer and show its potential for AI-RAN deployments.