Revolutionizing Wireless Networks with Self-Supervised Learning: A Pathway to Intelligent Communications
Zhixiang Yang, Hongyang Du, Dusit Niyato, Xudong Wang, Yu Zhou, Lei Feng, Fanqin Zhou, Wenjing Li, Xuesong Qiu
Abstract
With the rapid proliferation of mobile devices and data, next-generation wireless communication systems face stringent requirements for ultra-low latency, ultra-high reliability, and massive connectivity. Traditional artificial intelligence (AI)-driven wireless network designs relying on supervised learning, while promising, often suffer from labeled data dependency and struggle with generalization. To address these challenges, we present an integration of self-supervised learning (SSL) into wireless networks. SSL leverages large volumes of unlabeled data to train models, enhancing scalability, adaptability, and generalization. This article offers a comprehensive overview of SSL, categorizing its application scenarios in wireless network optimization and presenting a case study on its impact on semantic communication. Our findings high-light the potential of SSL to significantly improve wireless network performance without extensive labeled data, paving the way for more intelligent and efficient communication systems.