Joint Optimization of Resources in Fog-Radio Access Network with Binary Computation Offloading

With the dramatic increase in the number of emerging Internet services, the Fog-Radio Access Network (F-RAN) has recently emerged as a promising paradigm to enhance high-load task processing capabilities for mobile devices, such as the Internet of things (IoT) and mobile terminals. Hence, it becomes...

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Bibliographic Details
Published in:Mobile information systems Vol. 2023; pp. 1 - 15
Main Authors: Bai, Wenle, Wang, Zhuoqi
Format: Journal Article
Language:English
Published: Amsterdam Hindawi 21.04.2023
John Wiley & Sons, Inc
Subjects:
Algorithms
Communication
Computation offloading
Convex analysis
Decision making
Deep learning
Edge computing
Electronic devices
Energy consumption
Game theory
Integer programming
Internet of Things
Mixed integer
Neural networks
Nonlinear programming
Optimization
Reputations
Resource allocation
Servers
Strategy
ISSN:1574-017X, 1875-905X
Online Access:Get full text
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Summary:With the dramatic increase in the number of emerging Internet services, the Fog-Radio Access Network (F-RAN) has recently emerged as a promising paradigm to enhance high-load task processing capabilities for mobile devices, such as the Internet of things (IoT) and mobile terminals. Hence, it becomes a challenge for the F-RAN to reduce the offloading cost by designing an effective offloading strategy and rational planning of limited network resources to improve the quality of experience (QoE). This article investigates the F-RAN with a binary offload policy. It proposes an intelligent algorithm capable of optimally adapting to task offload policy, fog computing resource allocation, and offload channel resource allocation. To evaluate the offloading strategy intuitively, we design a system utility metric defined as a delay-energy weighted sum. The joint optimization problem is converted into a convex problem based on this metric, i.e., a mixed integer nonlinear programming (MINLP) problem. A novel algorithm based on improved double-deep Q neural networks is DDQN, which is proposed to address this problem. Furthermore, an action space mapping method in the DDQN framework is presented to obtain offloading decisions. Extensive experimental data indicate that the proposed DDQN algorithm can effectively reduce the offloading cost and is adaptable to different offloading scenarios.
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ISSN:1574-017X
1875-905X
DOI:10.1155/2023/1687672