计算卸载
计算机科学
强化学习
分布式计算
移动边缘计算
小细胞
架空(工程)
利用
蜂窝网络
计算
延迟(音频)
无线网络
能源消耗
边缘计算
计算机网络
无线
GSM演进的增强数据速率
服务器
工程类
人工智能
电信
算法
电气工程
操作系统
计算机安全
作者
Xiaoyan Huang,Supeng Leng,Sabita Maharjan,Yan Zhang
出处
期刊:IEEE Transactions on Vehicular Technology
[Institute of Electrical and Electronics Engineers]
日期:2021-09-01
卷期号:70 (9): 9282-9293
被引量:82
标识
DOI:10.1109/tvt.2021.3096928
摘要
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.
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