摘要
针对室内无线网络中的能量消耗过大问题,提出了一种基于深度Q学习的家庭基站发射功率分配算法。首先构造深度学习网络(DLN),优化室内无线网络的能量效率;然后将能量消耗指数作为奖罚值,利用批量梯度下降法不断地训练DLN的权值。最后仿真结果表明,所提出的算法可以动态调整发射功率,在收敛速度和能量消耗优化方面明显优于Q学习算法和注水算法,可以有效地降低室内无线网络的能耗。
To overcome the serve energy consumption problem in indoor wireless networks,a deep Q-Learning based transmit power allocation algorithm for home base station is proposed.Firstly,a deep learning network(DLN)is built to optimize the energy efficiency of indoor wireless networks.Then,the energy consumption rating is regarded as the rewards,the batch gradient descent method is used to continuously train the weights of DLN.Finally,the simulation results show that the proposed algorithm can dynamically adjust the transmit power,and is significantly superior to Q-Learning and water-filling algorithms in terms of convergence speed and EC optimization,which can effectively reduce the energy consumption of indoor wireless networks.
作者
吕亚平
贾向东
路艺
敬乐天
LÜYa-ping;JIA Xiang-dong;LU Yi;JING Le-tian(College of Computer Science and Engineering,Northwest Normal University,Lanzhou 730070;Wireless Communication Key Laboratory of Jiangsu Province,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)
出处
《计算机工程与科学》
CSCD
北大核心
2021年第7期1250-1255,共6页
Computer Engineering & Science
基金
国家自然科学基金(61861039)
甘肃省科技计划(18YF1GA060)
西北师范大学青年教师科研能力提升计划创新团队项目“下一代无线网络关键技术”。
关键词
室内无线网络
能量消耗
功率分配
深度Q学习
indoor wireless network
energy efficiency
power allocation
deep Q-Learning