L. Zhang, Y. Jiang, F. -C. Zheng, M. Bennis and X. You, "Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach," 2022 IEEE International Conference on Communications Workshops (ICC Workshops), Seoul, Korea, Republic of, 2022, pp. 97-102, doi: 10.1109/ICCWorkshops53468.2022.9814649
Computation offloading and resource allocation in F-RANs : a federated deep reinforcement learning approach
|Author:||Zhang, Lingling1; Jiang, Yanxiang1,2; Zheng, Fu-Chun1,2;|
1National Mobile Communications Research Laboratory, Southeast University, Nanjing, China
2School of Electronic and Information Engineering, Harbin Institute of Technology, Shenzhen, China
3Centre for Wireless Communications, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023021026767
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-02-10
The fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs). Due to the limited resource of F-APs, it is important to design an efficient task offloading scheme. In this paper, by considering time-varying network environment, a dynamic computation offloading and resource allocation problem in F-RANs is formulated to minimize the task execution delay and energy consumption of MDs. To solve the problem, a federated deep reinforcement learning (DRL) based algorithm is proposed, where the deep deterministic policy gradient (DDPG) algorithm performs computation offloading and resource allocation in each F-AP. Federated learning is exploited to train the DDPG agents in order to decrease the computing complexity of training process and protect the user privacy. Simulation results show that the proposed federated DDPG algorithm can achieve lower task execution delay and energy consumption of MDs more quickly compared with the other existing strategies.
IEEE International Conference on Communications workshop
|Pages:||97 - 102|
2022 IEEE International Conference on Communications Workshops (ICC Workshops)
IEEE International Conference on Communications Workshops
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the National Key Research and Development Program under Grant 2021YFB2900300, the National Natural Science Foundation of China under grant 61971129, and the Shenzhen Science and Technology Program under Grant KQTD20190929172545139.
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