A survey on the applications of machine learning, deep learning, and reinforcement learning in wireless communications
Huu Quy Tran, Viet–Thanh Pham, Sy-Toan Ngo · Computer and Telecommunication Engineering · 2025
This survey explores the integration of machine learning (ML), deep learning (DL), and reinforcement learning (RL) within wireless communications. It reviews various methods, algorithms, and applications while addressing the challenges and future research directions in this field. The paper highlights the necessity of intelligent techniques to enhance the performance and management of wireless networks, driven by the increasing complexity and demand for higher efficiency. Key areas of focus include network optimization, resource management, security, signal recognition, channel coding, traffic prediction, access control, and energy optimization. The survey also discusses emerging techniques such as federated learning, transfer learning, and multi-agent reinforcement learning, emphasizing their potential to revolutionize wireless communication systems.