Difference between revisions of "5G OAI Neural Receiver Testbed with USRP X410"

Revision as of 19:48, 30 October 2025

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.

Real-Time Benchmarking Platform

To validate the performance of the neural receiver in real hardware, the prototype integrates:

• The OAI real-time 5G protocol stack (complete core, RAN, and UE) running on commodity CPUs.
• NI USRP SDR hardware as the RF front-end.
• An optional O-RAN Near-RT RIC (via FlexRIC) integration.
• Neural receiver inference performed on a GPU (e.g., Nvidia A100, RTX 4070, RTX 4090), accessed via TensorFlow RT C-API for seamless integration within OAI.

This setup enables a direct comparison between the traditional receiver baseline against the neural receiver in an end-to-end real-time system.

Benchmarking Results

Initial testing focuses on uplink performance using various MCS levels (MCS-11, MCS-15, MCS-20 are specifically highlighted in this document) and SNR ranges (5 dB to 18 dB) under a realistic fading channel profile (urban micro, 2 m/s, 45ns delay spread). Each measurement is averaged over 300 transport blocks.

Some of the key findings are listed below.

• The neural receiver shows a clear Bit Error Rate (BER) advantage at lower MCS and lower SNR.
• At higher MCS levels, the performance gap narrows (a trade-off that merits further analysis).
• A reduced uplink bandwidth was used to meet strict real-time latency requirements (500 μs slot duration with 30 KHz SCS).
• The neural receiver model complexity was reduced by 15 times (from 700K to 47K parameters) to achieve real-time GPU inference.

These results underscore the crucial balance between complexity, latency, and performance in AI-enhanced wireless physical-layer deployments.

Conclusions and Implications

The testbed demonstrates a realistic path from simulation to real-time deployment of neural receiver models. This workflow supports rapid prototyping, robust AI model validation, and exploration of architecture-performance trade-offs.

Some key takeaways are listed below.

• AI/ML models can be efficiently integrated into real-time wireless stacks using SDR hardware and GPU inference.
• Low-complexity models offer promising performance improvements while satisfying real-time constraints.
• Synchronized dataset generation and automated test workflows enable scalable ML benchmarking across scenarios.
• The framework allows researchers to investigate unexpected behaviors and robustness in AI-native wireless systems.

Ultimately, the methodology bridges AI/ML conceptual research and realistic deployment, advancing trust and utility in AI-powered future wireless systems.

Hardware Overview

The Universal Software Radio Peripheral (USRP) devices from NI (an Emerson company) are software-defined radios which are widely used for wireless research, prototyping, and education. The hardware specifications for the various USRP devices are listed elsewhere on this Knowledge Base (KB). For this Neural Receiver implementation described in this document, we use the USRP X410. The USRP X440 may also be used, with some further adjustments to the system configuration.

The resources for the USRP X410 are listed below.

The Hardware Resource page for the USRP X410 can be found here.

The product page for the USRP X410 can be found here.

The User Manual for the USRP X410 can be found here.

The resources for the USRP X440 are listed below.

The Hardware Resource page for the USRP X410 can be found here.

The product page for the USRP X410 can be found here.

The User Manual for the USRP X410 can be found here.

The USRP X410 is connected to the host computer using a single QSFP28 100 Gbps Ethernet link, or using a QSFP28-to-SFP28 breakout cable, which provides four 25 Gbps SFP28 Ethernet links. On the host computer, a 100 Gbps or 25 Gbps Ethernet network card is used to connect to the USRP.

The USRP X410 devices are synchronized with the use of a 10 MHz reference signal and a 1 PPS signal, distributed from a common source. This can be provided by the OctoClock-G (see here and here for more information).

For control and management of the USRP X410, a 1 Gbps Ethernet connection to the host computer is needed, as well as a USB serial console connection.

Further details of the hardware configuration will be discussed later in this document.

Software Overview

The software stack running on the computers used in this implementation are as listed below.

• Ubuntu 22.04.5, running on-the-metal, and not in any Virtual Machine (VM)
• UHD version 4.8.0.0
• Nvidia drivers version 535
• Nvidia CUDA version 12.2
• TensorFlow 2.14

For the OAI gNB, the OAI UE, and the FlexRIC, there will be NI-specific versions used, and these will be obtained from an NI repository on GitHub.

Note that the Data Plane Development Kit (DPDK) is not used in this implementation. However, it may may be helpful when using higher sampling rates.

Further details of the software configuration will be discussed later in this document.

AI in 6G

The figure listed below highlights the vision for sixth-generation (6G) wireless systems. Beyond incremental improvements, 6G introduces three major advances, as listed below.

• Spectrum Expansion: Extending from traditional sub-6 GHz and mmWave bands into FR3 (7 to 24 GHz) and sub-THz (up to 300 GHz), enabling ultra-wide bandwidths and unprecedented data rates.

• New Applications: Integration of non-terrestrial networks (NTN) with terrestrial infrastructure and joint communication-and-sensing (JCAS) functionalities, supporting use cases such as connected vehicles, satellite-augmented IoT, and immersive XR.

• Network Optimization: Advancements in massive MIMO, multi-user beamforming, and Open RAN disaggregation, improving spectral efficiency, flexibility, and energy sustainability.