AI-Based Spectrum Sensing with Nvidia Jetson and USRP

Application Note Number and Authors

AN-811

Authors

Bharat Agarwal and Neel Pandeya

Executive Summary

This application note presents a complete framework for real-time spectrum sensing using NI Universal Serial Radio Peripheral (USRP) Software-Defined Radios (SDRs) and NVIDIA Jetson or standard x86 compute platforms. The framework is not limited to a single USRP model—the X410, X310, and B2xx series (e.g., B206) can all be used as transmitters or receivers depending on the deployment scenario. The solution leverages the NI-RF Data Recording API to enable scalable RF data acquisition, SigMF-compliant metadata tagging, and seamless integration with machine learning workflows.

The document outlines three core usage scenarios:

1. x86-Based Development Workflow: Using a workstation or server-class x86 machine, paired with high-end USRPs such as the X410 or X310, the system supports wideband spectrum sensing (up to 400 MHz instantaneous bandwidth per channel). This configuration is ideal for laboratory development, algorithm training, and high-throughput dataset generation.
2. Jetson-Based Embedded Sensing (Primary Use Case): Using an NVIDIA Jetson platform as the host (e.g., AGX Orin) with a compact B206 SDR as receiver and an X410 as transmitter, the system delivers efficient edge inference with GPU acceleration. Although the B206 limits the instantaneous bandwidth to 56 MHz, this configuration emphasizes portability, low power, and real-time embedded operation.
3. User-Defined Dataset Integration: In addition to live spectrum sensing, the framework supports integration and generation of user-defined datasets. This functionality extends the applicability of the system beyond real-time capture, enabling flexible experimentation, reproducibility, and seamless AI/ML dataset preparation. Two complementary capabilities are supported:
   1. SigMF Dataset Recording
      • All captured RF data is stored in the Signal Metadata Format (SigMF).
      • SigMF pairs raw IQ samples (.sigmf-data) with a corresponding metadata file (.sigmf-meta) in JSON format.
      • Metadata describes acquisition parameters such as frequency, bandwidth, gain, device type, timestamps, and scenario context.
      • Being human-readable and portable, SigMF datasets can be used across a wide range of software environments, making them ideal for wireless research, spectrum monitoring, AI/ML training for 6G, and regulatory validation.
      • Example: A spectrum sensing session at 3.5 GHz, 20 MHz bandwidth, and 10-second duration will result in a SigMF-compliant dataset ready for further processing or ML-based classification.
   2. Continuous Waveform Playback with User-Defined Files
      • The platform supports continuous transmission and replay of user-defined waveforms in TDMS or MATLAB (.mat) formats.
      • This allows testing with standard-compliant signals such as 5G NR, LTE, Radar, or Wi-Fi, or custom-designed waveforms.
      • By replaying predefined waveforms, researchers can benchmark algorithms, validate coexistence scenarios, and reproduce experiments consistently across testbeds.
      • Example: A MATLAB-generated LTE downlink frame can be continuously transmitted via an X410 while a B206 or X310 records the received signal in SigMF format for classification.

Together, these capabilities ensure that the NI-RF Data Recording API can handle both dataset creation (SigMF-based recording) and waveform-driven experimentation (TDMS/MAT playback), thereby covering the entire pipeline from signal generation to ML-ready dataset production.

By combining NI's reliable SDR hardware with NVIDIA's efficient edge compute platforms and a unified data interface, this solution supports a wide range of spectrum intelligence applications—from interference detection and dynamic spectrum access to embedded RF analytics. The methodology enables scalable deployment from lab to field, supporting real-time insights and long-term data collection in a streamlined, modular pipeline.

Executive Summary

Overview

This Application Note presents ...