Channel Modeling and Generation: Train Generative Networks and Generate 6G Channel Data

In the upcoming 6G era, with the deployment of massive Multi-input Multi-output (MIMO) systems, collecting and capturing 6G channel data through traditional channel modeling methods is very expensive. In addition, wireless communication carriers continuously propose and use artificial intelligence (AI) and deep learning (DL) based wireless communication solutions. Implementing such AI and DL based solutions requires a certain amount of high-quality channel data as a prerequisite. Traditional channel modeling methods cannot meet the requirements of simulating or collecting channel data rapidly and efficiently. In this paper, a generative network for channel modeling and signal generation, two data augmentation methods and a training technique are proposed. In short, this paper covers how to improve the performance of generative networks and how to generate high quality data with the premise that a large amount of channel samples are limited. Finally, the experimental results show that our proposed network could effectively and quickly generate 6G channel data by achieving the highest final score on both simple and complex testset. And the simulation results show that the generated data by our proposed structure has consistent normalized power with the real data. And the generated data can support a wide variety of AI-based wireless communication tasks.