  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.
