基于深度Q学习的室内无线网络资源分配算法

A deep Q-Learning based resource allocation algorithm in indoor wireless networks

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.