Generative-Artificial-Intelligence-Based Wireless Channel Modeling: Challenges and Opportunities

Abstract

Wireless channel modeling is critical to understanding and optimizing signal transmission. Wireless channels are influenced by many factors, including path loss, reflection, fading, and interference, making them complex and difficult to model and predict. Although some traditional wireless channel modeling methods are effective, they have limitations in handling complex multipath effects and nonlinear characteristics. Generative artificial intelligence (GAI) has the ability to generate robust data and therefore can be potentially used to model realistic wireless channel characteristics. However, there exist some challenges in GAI for wireless channel modeling, including physical layer network security issues, limited generalization ability, and the bandwidth consumption of centralized training. This article introduces a GAI-based wireless channel modeling framework, which leverages blockchain and federated learning to address efficiency, network security, and data privacy concerns in GAI channel modeling. Blockchain technology, through distributed ledgers and smart contracts, enables resource sharing and efficient utilization, enhancing computational efficiency and reducing network security risks. Federated learning allocates training tasks to different devices and nodes, thus allowing model training without centralizing data, protecting user privacy, and reducing network bandwidth usage. This article demonstrates the performance and effectiveness of the proposed framework through numerical results.