Cognitive Radio (CR) systems, which dynamically adapt to changing spectrum environments, could benefit significantly from advancements in machine learning technologies. These systems can be enhanced in terms of spectral efficiency, robustness, and security through innovative approaches such as the use of Transformer models. This work investigates the application of Transformer models, specifically the GPT-2 architecture, to generate novel modulation schemes for wireless communications. By training a GPT-2 model on a dataset of existing modulation formulas, new modulation schemes has been created. These generated schemes are then compared to traditional methods using key performance metrics such as Signal-to-Noise Ratio (SNR) and Power Spectrum Density (PSD). The results show that Transformer-generated modulation schemes can achieve performance comparable to, and in some cases outperforming, traditional methods. This demonstrates that advanced CR systems could greatly benefit from the implementation of Transformer models, leading to more efficient, robust, and secure communication systems.

翻译：认知无线电（CR）系统能够动态适应变化的频谱环境，机器学习技术的进步可为其带来显著增益。通过采用Transformer模型等创新方法，此类系统在频谱效率、鲁棒性和安全性方面均能得到提升。本研究探讨了Transformer模型（特别是GPT-2架构）在无线通信新型调制方案生成中的应用。通过在现有调制公式数据集上训练GPT-2模型，成功创建出新型调制方案。随后利用信噪比（SNR）和功率谱密度（PSD）等关键性能指标，将生成方案与传统方法进行对比。结果表明，Transformer生成的调制方案能达到与传统方法相当的性能，在某些情况下甚至表现更优。这证明先进的CR系统可通过引入Transformer模型获得显著性能提升，从而构建更高效、鲁棒且安全的通信系统。