文献类型：期刊文献

英文题名：A Resource Allocation Algorithm for Ultra-Dense Networks Based on Deep Reinforcement Learning

作者：Zhang, H. S.[1];Wang, T. M.[1];Shen, H. W.[1]

通讯作者：Wang, TM[1]

机构：[1]Beijing Union Univ, Coll Appl Sci & Technol, Beijing 100101, Peoples R China

第一机构：北京联合大学应用科技学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Appl Sci & Technol, Beijing 100101, Peoples R China.|[1141775]北京联合大学应用科技学院;[11417]北京联合大学;

年份：2021

卷号：16

期号：2

起止页码：1-11

外文期刊名：INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL

收录：;Scopus(收录号：2-s2.0-85104066147);WOS:【SCI-EXPANDED(收录号:WOS:000636782700009)】；

基金：The APC was funded by R&D center "Cercetare Dezvoltare Agora" of Agora University.

语种：英文

外文关键词：ultra-dense networks (UDNs); deep reinforcement learning (DRL); resource allocation; throughput; energy efficiency

摘要：The resource optimization of ultra-dense networks (UDNs) is critical to meet the huge demand of users for wireless data traffic. But the mainstream optimization algorithms have many problems, such as the poor optimization effect, and high computing load. This paper puts forward a wireless resource allocation algorithm based on deep reinforcement learning (DRL), which aims to maximize the total throughput of the entire network and transform the resource allocation problem into a deep Q-learning process. To effectively allocate resources in UDNs, the DRL algorithm was introduced to improve the allocation efficiency of wireless resources; the authors adopted the resource allocation strategy of the deep Q-network (DQN), and employed empirical repetition and target network to overcome the instability and divergence of the results caused by the previous network state, and to solve the overestimation of the Q value. Simulation results show that the proposed algorithm can maximize the total throughput of the network, while making the network more energy-efficient and stable. Thus, it is very meaningful to introduce the DRL to the research of UDN resource allocation.