As a revolutionary generative paradigm of deep learning, generative
adversarial networks (GANs) have been widely applied in various fields to
synthesize realistic data. However, it is challenging for conventional GANs to
synthesize raw signal data, especially in some complex cases. In this paper, we
develop a novel GAN framework for radio generation called "Radio GAN". Compared
to conventional methods, it benefits from three key improvements. The first is
learning based on sampling points, which aims to model an underlying sampling
distribution of radio signals. The second is an unrolled generator design,
combined with an estimated pure signal distribution as a prior, which can
greatly reduce learning difficulty and effectively improve learning precision.
Finally, we present an energy-constrained optimization algorithm to achieve
better training stability and convergence. Experimental results with extensive
simulations demonstrate that our proposed GAN framework can effectively learn
transmitter characteristics and various channel effects, thus accurately
modeling for an underlying sampling distribution to synthesize radio signals of
high quality.

本文提出了一种基于采样点和未卷积生成器设计的无线电 GAN 框架，并结合估计的纯信号分布作为先验分布，通过能量约束优化算法实现更好的训练稳定性和收敛性，在广泛的仿真实验中展示了该框架可以有效地学习发射机特性和各种通道效应，从而准确地建模基础采样分布以合成高质量的无线电信号。