CR-GAN Complex Wireless Channel Modeling with Hidden Space Sampling and Hidden Feature Extraction
To accurately model random wireless channels,an adaptive channel modeling framework based on a strengthened conditional generative adversarial network (GAN) is proposed. It utilizes the extended GAN for training to ap-proximately estimate the response of wireless channels and thus stimulate the actual wireless channels. To improve both the GAN training stability and learning capability,conditional information and gradient penalty terms are introduced. Besides,a strengthened conditional GAN frame,named condition reinforcement GAN (CR-GAN),is proposed to extract the essential hidden characteristics of wireless channels. In addition,a hidden space sampling strategy is utilized to increase the mutual information between the potential variables and generative data for the improved channel modeling performance of the pro-posed framework. Simulation results demonstrate that,at a signal-to-noise ratio of 10dB,the proposed CR-GAN frame-work outperforms current GAN-based models by reducing 24％ of the normalized mean squared error.
万方数据
维普
