Demo: Real-Time Spectrum Segmentation and Classification with Over-The-Air Data

Spectrum usage is increasing daily, necessitating new methods for efficient utilization. Spectrum sharing allows the coexistence of multiple wireless communication systems in the same spectrum. Effective spectrum segmentation and classification are essential for this, yet existing methods treat them as separate processes and often focus on specific communication techniques. Our application addresses these issues by jointly segmenting and classifying narrowband signals in wideband IQ samples. This demo paper presents the application, demonstrating its end-to-end approach of spectrum segmentation and classification. The application achieves an accuracy of 92.6% on the over-the-air (OTA) wireless communication spectrum. This represents an improvement of over 9% compared to the state-of-the-art solution, highlighting its effectiveness.