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CLC number: TN929.5

On-line Access: 2022-10-24

Received: 2021-07-09

Revision Accepted: 2022-10-24

Crosschecked: 2021-10-19

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Xueyan CAO




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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.10 P.1546-1561


Resource allocation for network profit maximization in NOMA-based F-RANs: a game-theoretic approach

Author(s):  Xueyan CAO, Shi YAN, Hongming ZHANG

Affiliation(s):  State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Corresponding email(s):   2013212868@bupt.edu.cn, yanshi01@bupt.edu.cn, zhanghm@bupt.edu.cn

Key Words:  Fog radio access network, Non-orthogonal multiple access, Game theory, Cache placement, Resource allocation

Xueyan CAO, Shi YAN, Hongming ZHANG. Resource allocation for network profit maximization in NOMA-based F-RANs: a game-theoretic approach[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(10): 1546-1561.

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T1 - Resource allocation for network profit maximization in NOMA-based F-RANs: a game-theoretic approach
A1 - Xueyan CAO
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DOI - 10.1631/FITEE.2100341

non-orthogonal multiple access (NOMA) based fog radio access networks (F-RANs) offer high spectrum efficiency, ultra-low delay, and huge network throughput, and this is made possible by edge computing and communication functions of the fog access points (F-APs). Meanwhile, caching-enabled F-APs are responsible for edge caching and delivery of a large volume of multimedia files during the caching phase, which facilitates further reduction in the transmission energy and burden. The need of the prevailing situation in industry is that in NOMA-based F-RANs, energy-efficient resource allocation, which consists of cache placement (CP) and radio resource allocation (RRA), is crucial for network performance enhancement. To this end, in this paper, we first characterize an NOMA-based F-RAN in which F-APs of caching capabilities underlaid with the radio remote heads serve user equipments via the NOMA protocol. Then, we formulate a resource allocation problem for maximizing the defined performance indicator, namely network profit, which takes caching cost, revenue, and energy efficiency into consideration. The NP-hard problem is decomposed into two sub-problems, namely the CP sub-problem and RRA sub-problem. Finally, we propose an iterative method and a Stackelberg game based method to solve them, and numerical results show that the proposed solution can significantly improve network profit compared to some existing schemes in NOMA-based F-RANs.




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