Sklar Jack, Wunderlich Adam
Communications Technology Laboratory, National Institute of Standards and Technology, Boulder, CO 80305, USA.
J Res Natl Inst Stand Technol. 2022 Feb 14;126:126046. doi: 10.6028/jres.126.046. eCollection 2021.
High-quality recordings of radio frequency (RF) emissions from commercial communication hardware in realistic environments are often needed to develop and assess spectrum-sharing technologies and practices, e.g., for training and testing spectrum sensing algorithms and for interference testing. Unfortunately, the time-consuming, expensive nature of such data collections together with data-sharing restrictions pose significant challenges that limit data set availability. Furthermore, developing accurate models of real-world RF emissions from first principles is often very difficult because system parameters and implementation details are at best only partially known, and complex system dynamics are difficult to characterize. Hence, there is a need for flexible, data-driven methods that can leverage existing data sets to synthesize additional similar waveforms. One promising machine-learning approach is unsupervised deep generative modeling with generative adversarial networks (GANs). To date, GANs for RF communication signals have not been studied thoroughly. In this paper, we present the first in-depth investigation of generated signal fidelity for GANs trained with baseband orthogonal frequency-division multiplexing (OFDM) signals, where each subcarrier is digitally modulated with quadrature amplitude modulation (QAM). Building on prior GAN methods, we developed two novel GAN models and evaluated their performance using simulated data sets with known ground truth. Specifically, we investigated model performance with respect to increasing data set complexity over a range of OFDM parameters and conditions, including fading channels. The findings presented here inform the feasibility of use cases and provide a foundation for further investigations into deep generative models for RF communication signals.
在现实环境中，为了开发和评估频谱共享技术与实践，例如用于训练和测试频谱感知算法以及进行干扰测试，通常需要高质量的商业通信硬件射频（RF）发射记录。不幸的是，此类数据收集既耗时又昂贵，再加上数据共享限制，带来了重大挑战，限制了数据集的可用性。此外，从第一原理出发开发精确的现实世界射频发射模型通常非常困难，因为系统参数和实现细节充其量也只是部分已知，而且复杂的系统动态难以表征。因此，需要灵活的数据驱动方法，能够利用现有数据集来合成额外的类似波形。一种有前途的机器学习方法是使用生成对抗网络（GAN）进行无监督深度生成建模。到目前为止，针对射频通信信号的GAN尚未得到深入研究。在本文中，我们首次对使用基带正交频分复用（OFDM）信号训练的GAN生成信号保真度进行了深入研究，其中每个子载波都采用正交幅度调制（QAM）进行数字调制。在先前的GAN方法基础上，我们开发了两种新颖的GAN模型，并使用具有已知真实情况的模拟数据集评估了它们的性能。具体而言，我们研究了在包括衰落信道在内的一系列OFDM参数和条件下，随着数据集复杂度增加时模型的性能。此处呈现的研究结果为用例的可行性提供了参考，并为进一步研究射频通信信号的深度生成模型奠定了基础。
