AI-Based Intelligent Resource Management 

We maximize resource efficiency through data-driven AI algorithms, overcoming the limitations of mathematical modeling in complex network environments.
Reinforcement Learning-Based Optimization: Researching algorithms that autonomously perform power control, beamforming, and interference management in D2D and cellular networks using Deep Reinforcement Learning (DRL) and Multi-Agent Reinforcement Learning (MADRL).
Machine Learning-Based QoS Assurance: Developing DNN (Deep Neural Network)-based dynamic power control and mode selection techniques to guarantee network jitter and service reliability.