Guest Editorial: Generative Foundation Models for Communications: Part 1

Conventional artificial intelligence (AI) has demonstrated significant capabilities in enhancing communication performance, notably through improving channel estimation, signal detection, and error correction. Leveraging its strengths in data analysis, compression, and feature extraction, traditional AI methods have effectively optimized these critical communication functions. However, as we progress towards the sixth generation (6G) of wireless networks, characterized by extraordinarily high data rates, extensive coverage, and ubiquitous network access, traditional AI techniques are increasingly challenged. Specifically, they face difficulties in handling the vast volume of data generated by billions of interconnected devices and in satisfying stringent real-time processing requirements inherent to dynamic and unpredictable wireless environments.