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.

翻译：在公民宽带无线电服务（CBRS）等共享频段中，对有效频谱管理和干扰抑制的需求日益增长，这需要稳健的雷达检测算法来保护军事传输免受商业无线传输的干扰。这些算法反过来依赖于大规模、多样化且经过仔细标注的频谱图数据集。然而，收集和标注真实世界的射频（RF）频谱图数据仍然是一个重大挑战，因为雷达信号稀少且出现频率低。这一挑战使得创建平衡的数据集变得困难，从而限制了该领域人工智能模型的性能和泛化能力。为解决这一关键问题，我们提出了一种基于扩散的生成模型，用于合成CBRS频段内融合LTE、5G和雷达信号的五种不同类别的逼真且多样化的频谱图。我们使用广泛接受的评估指标——结构相似性指数（SSIM）和峰值信噪比（PSNR），对生成的频谱图进行结构和统计保真度分析，以量化其与训练数据的差异。此外，我们证明，在生成的频谱图上进行预训练，通过实现$51.5\%$的更快收敛速度，显著提高了在真实世界雷达检测任务上的训练效率。