  As an entirely-new paradigm to design the communication systems, deep
learning (DL), an approach that the machine learns the desired wireless
function, has received much attention recently. In order to fully realize the
benefit of DL-aided wireless system, we need to collect a large number of
training samples. Unfortunately, collecting massive samples in the real
environments is very challenging since it requires significant signal
transmission overhead. In this paper, we propose a new type of data acquisition
framework for DL-aided wireless systems. In our work, generative adversarial
network (GAN) is used to generate samples approximating the real samples. To
reduce the amount of training samples required for the wireless data
generation, we train GAN with the help of the meta learning. From numerical
experiments, we show that the DL model trained by the GAN generated samples
performs close to that trained by the real samples.
