Joint Allocation on Communication and Computing Resources for Fog Radio Access Networks

To improve the user experience, an increasing number of mobile applications offload their computing tasks to servers with powerful computing capabilities. The fog radio access network (F-RAN) incorporates the concept of "fog computing" into the access network architecture, endowing an edge...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.108310-108323
Main Authors: Ma, Yingteng, Wang, Haijun, Xiong, Jun, Diao, Jietao, Ma, Dongtang
Format: Article
Language:English
Subjects:
access strategy
Algorithms
Applications programs
Cloud computing
Communication
Computation offloading
Computational geometry
Computational modeling
Computer architecture
Convex analysis
Convex functions
Convexity
Delays
Edge computing
Fog radio access network
Genetic algorithms
genetic convex optimization algorithm
Mixed integer
Mobile computing
Nonlinear programming
offload selection
Optimization
Polynomials
Radio
Radio access networks
Resource allocation
Resource management
Task analysis
User requirements
User satisfaction
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Summary:To improve the user experience, an increasing number of mobile applications offload their computing tasks to servers with powerful computing capabilities. The fog radio access network (F-RAN) incorporates the concept of "fog computing" into the access network architecture, endowing an edge network with computing, storage, communication and control functions. In this paper, we consider a multiple fog access point (F-AP) and a multiuser F-RAN, where each user generates two different tasks: communication and computation. To satisfy the diverse quality of service requirements of different users, we jointly optimize the spectrum access, computation offloading and radio resource allocation. The problem is modeled as a mixed integer nonlinear programming problem, which is difficult to solve. In view of this, we propose a genetic algorithm based on convex optimization, i.e., the genetic convex optimization algorithm (GCOA), which divides the mixed integer nonlinear programming problem into two parts, i.e., optimization and convex optimization, to solve it in polynomial time. Simulation results are provided to verify the effectiveness of the algorithm.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3000832