用Transformer生成新调制方案，性能媲美甚至超越传统方法。
Transformer-Based Cognitive Radio: Adaptive Modulation Strategies Using Transformer Models
- 用GPT-2模型学习已有调制公式，自动生成新调制方案。
- 生成方案在SNR和功率谱密度上表现接近或优于传统方法。
- 适合研究智能通信系统、自适应调制的学者与工程师。
认知无线电（CR）系统可通过动态适应频谱环境提升性能。本文探索将Transformer模型（特别是GPT-2架构）应用于无线通信调制方案生成。通过在现有调制公式数据集上训练GPT-2，生成了新型调制方案，并以信噪比（SNR）和功率谱密度（PSD）等关键指标进行评估。结果表明，生成的调制方案在性能上可与传统方法相当，部分场景下甚至更优。这证明了将Transformer模型引入认知无线电系统，有望显著提升通信效率、鲁棒性与安全性。
原文摘要 · Abstract (English)
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.
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