模拟与无监督生成对抗网络的正交频率分割多重复合通信信号的可行性
1Communications Technology Laboratory, National Institute of Standards and Technology, Boulder, CO 80305, USA.
概括
生成对抗网络 (GAN) 可以合成现实的射频 (RF) 信号,用于频谱共享研究. 这项研究探讨了GAN用于生成直角频率分割复杂化 (OFDM) 信号,显示了数据驱动的射频信号建模的前景.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 高质量的射频 (RF) 发射数据对于开发频谱共享技术至关重要.
- 由于成本,时间和数据限制,收集和共享射频数据具有挑战性.
- 由于未知的系统参数,从第一原理准确的射频发射建模是很困难的.
研究的目的:
- 研究使用生成对抗网络 (GANs) 来合成射频通信信号.
- 为了评估在基带直角频率分割复杂化 (OFDM) 信号上训练的GAN的信号保真度.
- 为RF信号生成中的数据驱动方法提供基础.
主要方法:
- 开发了两种用于射频信号生成的新型GAN模型.
- 在基带OFDM信号上训练有素的GANs,其调制为方格振幅调制 (QAM).
- 使用不同复杂度和色条件的模拟数据集评估模型性能.
主要成果:
- 证明了使用GANs来生成高保真射频信号的可行性.
- 分析了不同OFDM参数和通道条件的GAN性能.
- 确定了影响生成信号准确性的关键因素.
结论:
- GAN提供了一个有希望的数据驱动方法来克服RF数据收集的局限性.
- 开发的GAN模型为RF信号深度生成模型的未来研究提供了基础.
- 结果为GAN在频谱传感和干扰测试中的实际应用提供了信息.
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