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
Clicked: 4060
Citations: Bibtex RefMan EndNote GB/T7714
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.
@article{title="Resource allocation for network profit maximization in NOMA-based F-RANs: a game-theoretic approach",
author="Xueyan CAO, Shi YAN, Hongming ZHANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="10",
pages="1546-1561",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100341"
}
%0 Journal Article
%T Resource allocation for network profit maximization in NOMA-based F-RANs: a game-theoretic approach
%A Xueyan CAO
%A Shi YAN
%A Hongming ZHANG
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 10
%P 1546-1561
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100341
TY - JOUR
T1 - Resource allocation for network profit maximization in NOMA-based F-RANs: a game-theoretic approach
A1 - Xueyan CAO
A1 - Shi YAN
A1 - Hongming ZHANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 10
SP - 1546
EP - 1561
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100341
Abstract: 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.
[1]Bai WL, Yao T, Zhang HJ, et al., 2019. Research on channel power allocation of fog wireless access network based on NOMA. IEEE Access, 7:32867-32873.
[2]Boyd S, Vandenberghe L, 2004. Convex Optimization. Cambridge University Press, Cambridge, UK.
[3]Cao XY, Peng MG, Ding ZG, 2019. A game-theoretic approach of resource allocation in NOMA-based fog radio access networks. Proc 90th Vehicular Technology Conf, p.1-5.
[4]Dang T, Peng MG, 2019. Joint radio communication, caching, and computing design for mobile virtual reality delivery in fog radio access networks. IEEE J Sel Areas Commun, 37(7):1594-1607.
[5]Deng RL, Lu RX, Lai CZ, et al., 2016. Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Int Things J, 3(6):1171-1181.
[6]Ding ZG, Fan PZ, Poor HV, 2016. Impact of user pairing on 5G nonorthogonal multiple-access downlink transmissions. IEEE Trans Veh Technol, 65(8):6010-6023.
[7]Ding ZG, Fan PZ, Poor HV, 2019. Impact of non-orthogonal multiple access on the offloading of mobile edge computing. IEEE Trans Commun, 67(1):375-390.
[8]Dinkelbach W, 1967. On nonlinear fractional programming. Manag Sci, 13(7):492-498.
[9]Kong HB, Flint I, Wang P, et al., 2018. Fog radio access networks: Ginibre point process modeling and analysis. IEEE Trans Wirel Commun, 17(8):5564-5580.
[10]Li QP, Zhao JH, Gong Y, et al., 2019. Energy-efficient computation offloading and resource allocation in fog computing for Internet of Everything. China Commun, 16(3):32-41.
[11]Li ZD, Wang Y, Liu M, et al., 2019. Energy efficient resource allocation for UAV-assisted space-air-ground Internet of remote things networks. IEEE Access, 7:145348-145362.
[12]Liu BH, Liu CX, Peng MG, et al., 2020. Resource allocation for non-orthogonal multiple access-enabled fog radio access networks. IEEE Trans Wirel Commun, 19(6):3867-3878.
[13]Ng DWK, Lo ES, Schober R, 2012. Energy-efficient resource allocation in OFDMA systems with large numbers of base station antennas. IEEE Trans Wirel Commun, 11(9):3292-3304.
[14]Park S, Simeone O, Shitz SS, 2016. Joint optimization of cloud and edge processing for fog radio access networks. IEEE Trans Wirel Commun, 15(11):7621-7632.
[15]Peng MG, Zhang KC, Jiang JM, et al., 2015. Energy-efficient resource assignment and power allocation in heterogeneous cloud radio access networks. IEEE Trans Veh Technol, 64(11):5275-5287.
[16]Peng MG, Yan S, Zhang KC, et al., 2016. Fog-computing-based radio access networks: issues and challenges. IEEE Netw, 30(4):46-53.
[17]Peng MG, Quek TQS, Mao GQ, et al., 2020. Artificial-intelligence-driven fog radio access networks: recent advances and future trends. IEEE Wirel Commun, 27(2):12-13.
[18]Rai R, Zhu HL, Wang JZ, 2021. Performance analysis of NOMA enabled fog radio access networks. IEEE Trans Commun, 69(1):382-397.
[19]Shi Y, Wang JH, Letaief KB, et al., 2009. A game-theoretic approach for distributed power control in interference relay channels. IEEE Trans Wirel Commun, 8(6):3151-3161.
[20]Sun YH, Peng MG, Mao SW, et al., 2019. Hierarchical radio resource allocation for network slicing in fog radio access networks. IEEE Trans Veh Technol, 68(4):3866-3881.
[21]Xu C, Sheng M, Varma VS, et al., 2016. Wireless service provider selection and bandwidth resource allocation in multi-tier HCNs. IEEE Trans Commun, 64(12):5108-5124.
[22]Yan S, Qi L, Zhou YC, et al., 2020. Joint user access mode selection and content popularity prediction in non-orthogonal multiple access-based F-RANs. IEEE Trans Commun, 68(1):645-666.
[23]Yang ZH, Xu W, Pan YJ, et al., 2018. Energy efficient resource allocation in machine-to-machine communications with multiple access and energy harvesting for IoT. IEEE Int Things J, 5(1):229-245.
[24]Yao JJ, Ansari N, 2019. Joint content placement and storage allocation in C-RANs for IoT sensing service. IEEE Int Things J, 6(1):1060-1067.
[25]Yu Y, Bu XY, Yang K, et al., 2019. Green large-scale fog computing resource allocation using joint benders decomposition, Dinkelbach algorithm, ADMM, and branch-and-bound. IEEE Int Things J, 6(3):4106-4117.
[26]Zhai DS, Zhang RN, Cai L, et al., 2018. Energy-efficient user scheduling and power allocation for NOMA-based wireless networks with massive IoT devices. IEEE Int Things J, 5(3):1857-1868.
[27]Zhang HJ, Qiu Y, Long KP, et al., 2018. Resource allocation in NOMA-based fog radio access networks. IEEE Wirel Commun, 25(3):110-115.
[28]Zhang JX, Zhang X, Wang WB, 2016. Cache-enabled software defined heterogeneous networks for green and flexible 5G networks. IEEE Access, 4:3591-3601.
[29]Zhang P, Peng MG, Cui SG, et al., 2022. Theory and techniques for “intellicise” wireless networks. Front Inform Technol Electron Eng, 23(1):1-4.
[30]Zhou YC, Yan S, Peng MG, 2019. Content placement with unknown popularity in fog radio access networks. Proc IEEE Int Conf on Industrial Internet, p.361-367.
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