A Deep Reinforcement Learning-Based Power Control Scheme for the 5G Wireless Systems  

作　　者：Renjie Liang Haiyang Lyu Jiancun Fan 

机构地区：[1]School of Electronic and Information Engineering,Xi’an Jiaotong University,Xi’an,Shaanxi 710049,China [2]School of Cyber Science and Engineering,Xi’an Jiaotong University,Xi’an,Shaanxi 710049,China

出　　处：《China Communications》2023年第10期109-119,共11页中国通信（英文版）

摘　　要：In the fifth generation(5G)wireless system,a closed-loop power control(CLPC)scheme based on deep Q learning network(DQN)is introduced to intelligently adjust the transmit power of the base station(BS),which can improve the user equipment(UE)received signal to interference plus noise ratio(SINR)to a target threshold range.However,the selected power control(PC)action in DQN is not accurately matched the fluctuations of the wireless environment.Since the experience replay characteristic of the conventional DQN scheme leads to a possibility of insufficient training in the target deep neural network(DNN).As a result,the Q-value of the sub-optimal PC action exceed the optimal one.To solve this problem,we propose the improved DQN scheme.In the proposed scheme,we add an additional DNN to the conventional DQN,and set a shorter training interval to speed up the training of the DNN in order to fully train it.Finally,the proposed scheme can ensure that the Q value of the optimal action remains maximum.After multiple episodes of training,the proposed scheme can generate more accurate PC actions to match the fluctuations of the wireless environment.As a result,the UE received SINR can achieve the target threshold range faster and keep more stable.The simulation results prove that the proposed scheme outperforms the conventional schemes.

关 键 词：reinforcement learning closed-loop power control(CLPC) signal-to-interference-plusnoise ratio(SINR) 

分 类 号：TN929.5[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]