Evolution of Machine Learning in Wireless Networks

This paper reviews the thirty-year trajectory of machine learning, focusing on its evolution from basic algorithms to complex applications in wireless networks. The integration of machine learning has been pivotal in supporting advanced applications, including Internet of things (IoT) and cognitive radio networks, enhancing adaptive learning and decision-making capabilities.

Developments:

• Comprehensive analysis of ML Techniques: Supervised, Unsupervised, and Reinforcement Learning.
• Application in heterogeneous networks, IoT, and cognitive radios.
• Potential for future network optimizations and solutions.

The examination brings forth the effective strategies and potential future adaptations machine learning can offer to the ever-evolving wireless network landscape. It highlights the crucial role of machine learning in adapting to complex, heterogeneous networks and supporting a multitude of services.

Impact:

This review serves as a testament to the transformative impact of machine learning over decades, promising further innovations in network technologies and solutions that could better cater to contemporary demands.