Wireless Signal Enhancement Based on Generative Adversarial Networks

Abstract Compared to traditional signal enhancement strategies in wireless communication, the emerging route based on deep learning has been showing better potential adaptivity to dynamic effects of noise and interference conditions. In this paper, we design and establish a signal enhancement network based on the specialized Generative Adversarial Networks, which can adaptively learn the characteristics of signals and achieve a signal enhancement in time-varying systems. We design a customized object function, and the raw time-domain signal is added to the network as a condition to achieve the state of the art enhancement effect with the effect that the symbol information remains unchanged. Besides its robust learning ability to dynamic channel effects on the signal, it also has the excellently adversarial ability for signal jitter and skews, the network can still track the signal cognitively. Experiments show that our proposed network’s wireless signal enhancement effect is state of the art of all methods.