Abstract
The papers in this special section focus on the use of distributed machine learning for wireless communications. With the emergence of new application scenarios (e.g., real-time and interactive services and Internet of Things) and the fast development of smart terminals, wireless data traffic has increased drastically, and the existing wireless networks cannot completely meet the technical requirements of the next generation mobile communication networks, e.g., 6G. In recent years, machine learning-based methods have been considered as potential technologies for 6G, because in wireless communication systems, key issues behind synchronization, channel estimation, signal detection, and iterative decoding can be solved by well-designed machine learning algorithms. Currently, most wireless network machine learning solutions require the training data and learning process to be centralized in one or more data centers. However, these centralized machine learning methods expose disadvantages, e.g., privacy security, significant signaling overhead, increased implementation complexity, and high latency, which limit their practicality. The wireless networks of the future must make quicker and more reliable decisions at the network edge.