Joint User Access Mode Selection and Content Popularity Prediction in Non-Orthogonal Multiple Access-Based F-RANs

Non-orthogonal multiple access (NOMA) is regarded as a promising technology for the next-generation wireless communication system. Introducing NOMA into the fog radio access networks (F-RANs) is able to provide simultaneous transmissions to multiple users and significantly enhance F-RAN performance....

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Bibliographic Details
Published in:IEEE transactions on communications 2020-01, Vol.68 (1), p.654-666
Main Authors: Yan, Shi, Qi, Lin, Zhou, Yangcheng, Peng, Mugen, Rahman, G. M. Shafiqur
Format: Article
Language:English
Subjects:
5G mobile communication
Algorithms
Caching
Computer simulation
content popularity prediction
Cost function
Downlink
Edge computing
Fog radio access networks (F-RANs)
Game theory
Games
Machine learning
Machine learning algorithms
Modal choice
mode selection
NOMA
non-orthogonal multiple access (NOMA)
Nonorthogonal multiple access
Optimization
Performance enhancement
Resource management
Storage capacity
Wireless communication systems
Wireless communications
Wireless networks
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Summary:Non-orthogonal multiple access (NOMA) is regarded as a promising technology for the next-generation wireless communication system. Introducing NOMA into the fog radio access networks (F-RANs) is able to provide simultaneous transmissions to multiple users and significantly enhance F-RAN performance. However, due to the increasing number of users and the constraint of caching storage capacity, there exists a tradeoff between NOMA transmission performance and fronthaul saving. In this paper, a hierarchical game framework is presented to solve the joint optimization problem of user access mode selection and content popularity prediction in NOMA based F-RANs. More specifically, the access mode selection problem is formulated as an evolutionary game. The proposals' evolutionary payoff expressions are derived by stochastic geometry tool, and the cost functions are related to the fog access point (F-AP) content placement profile as well as the fronthaul constraint. Moreover, the problem of what contents the F-AP should cache is modeled as a content popularity prediction problem, and based on both local and global user request states, a machine learning algorithm is presented to solve it. Simulation results validate the accuracy of analytical results and demonstrate our proposed algorithms can further improve the performance of NOMA based F-RANs.
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2019.2950215