Deep Reinforcement Learning and Mobile Communication Resource Management: Algorithms, Progress, and Prospects

As one of the highly-efficient algorithms for resource management and optimization in mobile communications, deep reinforcement learning (DRL) integrates the ability of deep learning to extract low dimensional features from high dimensional data with the decision-making ability of reinforcement learning. First, the concepts and principles of DRL algorithms were introduced. Then, the resource management and optimization effect of DRL in different scenarios were summarized and analyzed. The technologies and scenarios included network slicing, cloud computing, fog computing, and mobile edge computing. Furthermore, based on the key research progress of DRL in mobile communication resource management, the open issues and challenges of DRL were discussed, and possible solutions were proposed. Finally, development trends and key research directions in the field of mobile communication resoure management were prospected.