期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 70, 期 9, 页码 9282-9293出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2021.3096928
关键词
Task analysis; Resource management; Microcell networks; Computational modeling; Servers; Interference; Reinforcement learning; Mobile edge computing; small cell networks; multi-agent deep reinforcement learning; computation offloading; interference coordination
资金
- National Natural Science Foundation of China [61941102, 62071092]
This paper proposes a method for joint design of computation offloading and interference coordination using a distributed multi-agent deep reinforcement learning scheme, which significantly reduces energy consumption and ensures latency requirements.
Integrating mobile edge computing (MEC) with small cell networks has been conceived as a promising solution to provide pervasive computing services. However, the interactions among small cells due to inter-cell interference, the diverse application-specific requirements, as well as the highly dynamic wireless environment make it challenging to design an optimal computation offloading scheme. In this paper, we focus on the joint design of computation offloading and interference coordination for edge intelligence empowered small cell networks. To this end, we propose a distributed multi-agent deep reinforcement learning (DRL) scheme with the objective of minimizing the overall energy consumption while ensuring the latency requirements. Specifically, we exploit the collaboration among small cell base station (SBS) agents to adaptively adjust their strategies, considering computation offloading, channel allocation, power control, and computation resource allocation. Further, to decrease the computation complexity and signaling overhead of the training process, we design a federated DRL scheme which only requires SBS agents to share their model parameters instead of local training data. Numerical results demonstrate that our proposed schemes can significantly reduce the energy consumption and effectively guarantee the latency requirements compared with the benchmark schemes.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据