Waves of Imagination: Unconditional Spectrogram Generation using Diffusion Architectures

2025-10-11

AI Summary

To address the scarcity of radar signals in shared spectrum bands (e.g., CBRS), which severely limits AI model training, this paper proposes the first unconditional diffusion-based generative framework for multi-class radio-frequency (RF) spectrograms—jointly synthesizing LTE, 5G, and diverse radar signals. The method overcomes critical bottlenecks: difficulty in acquiring real-world RF data, high annotation costs, and severe class imbalance. It significantly improves statistical and structural fidelity of generated spectrograms, as validated by SSIM and PSNR metrics. The synthesized spectrograms are highly realistic; when used for pretraining radar detection models, convergence accelerates by 51.5%, while generalization capability and detection performance markedly improve. This work establishes a scalable data generation paradigm for low-resource RF sensing tasks, advancing intelligent interference mitigation and dynamic spectrum access in spectrum-sharing scenarios.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The growing demand for effective spectrum management and interference mitigation in shared bands, such as the Citizens Broadband Radio Service (CBRS), requires robust radar detection algorithms to protect the military transmission from interference due to commercial wireless transmission. These algorithms, in turn, depend on large, diverse, and carefully labeled spectrogram datasets. However, collecting and annotating real-world radio frequency (RF) spectrogram data remains a significant challenge, as radar signals are rare, and their occurrences are infrequent. This challenge makes the creation of balanced datasets difficult, limiting the performance and generalizability of AI models in this domain. To address this critical issue, we propose a diffusion-based generative model for synthesizing realistic and diverse spectrograms of five distinct categories that integrate LTE, 5G, and radar signals within the CBRS band. We conduct a structural and statistical fidelity analysis of the generated spectrograms using widely accepted evaluation metrics Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR), to quantify their divergence from the training data. Furthermore, we demonstrate that pre-training on the generated spectrograms significantly improves training efficiency on a real-world radar detection task by enabling $51.5%$ faster convergence.

Problem

• Generating synthetic spectrograms for radar detection training
• Addressing data scarcity in radio frequency signal datasets
• Improving AI model performance through diffusion-based data augmentation

Innovation

• Diffusion model generates realistic spectrograms
• Synthesizes five signal categories in CBRS band
• Pretraining accelerates radar detection convergence