Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 6, Issue 2, Pages 1960-1971Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2018.2871020
Keywords
Artificial intelligence; communication mode selection; deep reinforcement learning (DRL); fog radio access networks (F-RANs); resource management
Categories
Funding
- State Major Science and Technology Special Project [2017ZX03001025-006, 2018ZX03001023-005]
- National Natural Science Foundation of China [61831002]
- National Program for Special Support of Eminent Professionals
- U.S. National Science Foundation [CNS-1702957]
Ask authors/readers for more resources
Fog radio access networks (F-RANs) are seen as potential architectures to support services of Internet of Things by leveraging edge caching and edge computing. However, current works studying resource management in F-RANs mainly consider a static system with only one communication mode. Given network dynamics, resource diversity, and the coupling of resource management with mode selection, resource management in F-RANs becomes very challenging. Motivated by the recent development of artificial intelligence, a deep reinforcement learning (DRL)-based joint mode selection and resource management approach is proposed. Each user equipment (UE) can operate either in cloud RAN (C-RAN) mode or in device-to-device mode, and the resource managed includes both radio resource and computing resource. The core idea is that the network controller makes intelligent decisions on UE communication modes and processors' on-off states with precoding for UEs in C-RAN mode optimized subsequently, aiming at minimizing long-term system power consumption under the dynamics of edge cache states. By simulations, the impacts of several parameters, such as learning rate and edge caching service capability, on system performance are demonstrated, and meanwhile the proposal is compared with other different schemes to show its effectiveness. Moreover, transfer learning is integrated with DRL to accelerate learning process.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available