文章基本信息

标题：MEC Computation Offloading-Based Learning Strategy in Ultra-Dense Networks
本地全文：下载
作者：Chunhong Duo ; Peng Dong ; Qize Gao 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2022
卷号：13
期号：6
页码：271
DOI：10.3390/info13060271
语种：English
出版社：MDPI Publishing
摘要：Mobile edge computing (MEC) has the potential to realize intensive applications in 5G networks. Through migrating intensive tasks to edge servers, MEC can expand the computing power of wireless networks. Fifth generation networks need to meet service requirements, such as wide coverage, high capacity, low latency and low power consumption. Therefore, the network architecture of MEC combined with ultra-dense networks (UDNs) will become a typical model in the future. This paper designs a MEC architecture in a UDN, which is our research background. First, the system model is established in the UDN, and the optimization problems is proposed. Second, the action classification (AC) algorithm is utilized to filter the effective action in Q-learning. Then, the optimal computation offloading strategy and resource allocation scheme are obtained using a deep reinforcement learning-based AC algorithm, which is known as the DQN-AC algorithm. Finally, the simulation experiments show that the proposed DQN-AC algorithm can effectively reduce the system weighted cost compared with the full local computation algorithm, full offloading computation algorithm and Q-learning algorithm.