5G OAI Neural Receiver Testbed with USRP X410
Contents
Application Note Number and Authors
AN-829
Authors
Bharat Agarwal and Neel Pandeya
Executive Summary
Overview
This Application Note presents a practical, system-level benchmarking platform leveraging NI USRP software-defined radios (SDRs) and the OpenAirInterface (OAI) 5G/NR stack for evaluating AI-enhanced wireless receivers in real-time. It addresses one of the key challenges in deploying AI/ML at the physical layer-ensuring reliable system performance under real-time constraints.
Motivation and Context
AI and ML techniques hold promise for improving both wireless and non-wireless KPIs across the stack, from core-level optimization (e.g., load balancing, power savings), to tightly-timed PHY/MAC innovations such as:
- ML-based digital predistortion to improve power efficiency
- Neural receivers for channel estimation and symbol detection with improved SNR tolerance
- Intelligent beam and positioning prediction, even under fast channel dynamics
Consortia such as 3GPP (Release-18/19) and O-RAN are actively defining how AI/ML can be incorporated into future cellular network standards.
Neural Receiver Model
We demonstrate a real-time implementation of a neural receiver that is based on a published model architecture called DeepRX, which replaces the traditional OFDM receiver blocks (channel estimation, interpolation, equalization, detection) with a single neural network that treats the time-frequency grid data as image-like input. Model training and validation are performed using the NVIDIA Sionna link-level simulator, and training data is stored using the open SigMF format for reproducibility.
More information about the SigMF file format can be found on the project website, on the Wikipedia page, and on the GitHub page.
