Research on dynamic resource allocation optimization in 6G networks based on deep reinforcement learning

This paper proposes a dynamic resource allocation optimization method based on deep reinforcement learning to address the challenges of signal interference, latency sensitivity, and energy consumption in 6G networks caused by ultra-dense deployment and diverse applications. First, it elaborates on the challenges posed by the surge in IoT devices and data traffic to traditional communication models, as well as the role of edge computing, fog computing, and emerging technologies such as Cybertwin in mitigating network latency and computational pressure. Then, the paper details the principles of using Deep Q-Network (DQN) and its variants for channel estimation, interference detection, and resource scheduling, analyzing the advantages of reinforcement learning methods such as value iteration and policy gradient in complex multi-cell scenarios. Additionally, it discusses the application of multi-agent collaboration in vehicular network resource allocation. Simulation results demonstrate that the proposed scheme significantly improves network transmission rate, energy efficiency, and system utility, offering better stability and robustness compared to traditional modular optimization methods. Finally, the paper explores the potential applications of deep reinforcement learning in more complex network environments and its integration with other optimization algorithms, providing theoretical foundations and technical support for the efficient operation of 6G networks.