A Resource Allocation Algorithm for Ultra-Dense Networks Based on Deep Reinforcement Learning

IAENG Internaitonal journal of computer science

article

Huashuai Zhang¹  Tingmei Wang¹  Haiwei Shen¹ 

[1] College of Applied Science and Technology Beijing Union University Beijing 100101

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

DOI  :  10.15837/ijccc.2021.2.4189

学科分类：计算机科学（综合）

来源: International Association of Engineers

【 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.

【 授权许可】

CC BY