随着科技的快速发展，人们对无线频谱的需求越来越高。然而，由于频谱资源有限，如何有效利用这些资源成为 了无线电领域的一大挑战。为了解决这个问题，建立了一个主次用户共享相同的频谱资源，并且都以非协作方式工作的认知 无线网络模型，以提高次用户的吞吐量，然后使用基于SumTree采样深度Q学习（SumTree Deep Q-Network，ST-DQN）算法 来进行功率控制，来确保样本选取的优先级与多样性。最后通过 Python 进行了一系列的仿真实验，与传统的 Q 学习（qlearning）和自由探索算法在奖励、损失函数和次用户吞吐量等性能指标进行了比较与分析，研究发现ST-DQN算法在功率控 制方面表现更优。

With the rapid development of technology, people's demand for wireless spectrum is increasing. However, due to limited spectrum resources, how to effectively utilize these resources has become a major challenge in the field of radio. To address this is‐ sue, we establish a cognitive wireless network model where primary and secondary users share the same spectrum resources and work in a non cooperative manner to improve the throughput of secondary users. Then, we use the SumTree Sampling Deep QNetwork (ST-DQN) algorithm for power control to ensure priority and diversity in sample selection. Finally, a series of simulation experiments are conducted using Python to compare and analyze the performance indicators of reward, loss function, and sub user throughput with traditional Q-learning and free exploration algorithms. We find that the ST-DQN algorithm performs better in power control.