In the digital world today, cellular networks and their operators play a competitive and important role in communications. The bases of the competition of operators are the quality of provided services and the coverage level of their antennas, thereby attract customers. This paper studies cellular networks with two old and new operators and under the influence of government intervention in one area. Due to the high cost of building an antenna, the new operator participates in the cost of the infrastructure of the old operator to use the services of these antennas for their customers. On the other hand, the government considers incentive schemes to support mobile operators. The government plans to take part in the infrastructure costs of the old operator, and will receive the income tax from it. Hence, the new operator will go off from paying tax. The government subsidy contract with the old operator is based on the coverage level of the antenna and supports the operator to increase the coverage level. By doing so, the quality level of services and coverage development rate for the old operator increases, leading to increased demand and increased profits for this operator. On the other hand, as government support increases the demand for the old operator, the demand for the new operator decreases and the profit of the new operator decreases. Some numerical examples for Iranian telecommunication companies are applied to examine the applicability of the proposed models. Finally, sensitivity analysis on the main parameters is analyzed in-depth to extract some managerial implications.
Keywords: Game theory, cellular network operators, government intervention, cellular antennas, coverage development rate
@article{RO_2022__56_2_813_0,
author = {Fander, Atieh and Yaghoubi, Saeed and Tajik, Javad},
title = {A game theoretic model for cellular network operators{\textquoteright} cooperation under government intervention},
journal = {RAIRO. Operations Research},
pages = {813--829},
year = {2022},
publisher = {EDP-Sciences},
volume = {56},
number = {2},
doi = {10.1051/ro/2022025},
mrnumber = {4407595},
zbl = {1491.90028},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2022025/}
}
TY - JOUR AU - Fander, Atieh AU - Yaghoubi, Saeed AU - Tajik, Javad TI - A game theoretic model for cellular network operators’ cooperation under government intervention JO - RAIRO. Operations Research PY - 2022 SP - 813 EP - 829 VL - 56 IS - 2 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2022025/ DO - 10.1051/ro/2022025 LA - en ID - RO_2022__56_2_813_0 ER -
%0 Journal Article %A Fander, Atieh %A Yaghoubi, Saeed %A Tajik, Javad %T A game theoretic model for cellular network operators’ cooperation under government intervention %J RAIRO. Operations Research %D 2022 %P 813-829 %V 56 %N 2 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2022025/ %R 10.1051/ro/2022025 %G en %F RO_2022__56_2_813_0
Fander, Atieh; Yaghoubi, Saeed; Tajik, Javad. A game theoretic model for cellular network operators’ cooperation under government intervention. RAIRO. Operations Research, Tome 56 (2022) no. 2, pp. 813-829. doi: 10.1051/ro/2022025
[1] , and , Mobile channel and channel coordination under different supply chain contexts. Ind. Market. Manage. 84 (2020) 165–182. | DOI
[2] , and , On the use of multiobjective optimization for solving the location areas strategy with different paging procedures in a realistic mobile network. Appl. Soft Comput. 18 (2014) 146–157. | DOI
[3] , and , Optimizing the mobility management task in networks of four world capital cities. J. Network Comput. App. 51 (2015) 18–28. | DOI
[4] and , Using Bayesian networks to improve fault diagnosis during manufacturing tests of mobile telephone infrastructure. J. Oper. Res. Soc. 59 (2008) 423–430. | Zbl | DOI
[5] and , Maximum lifetime routing in wireless sensor networks. IEEE/ACM Trans. Networking 12 (2004) 609–619. | DOI
[6] , , , and , Strong minimum energy topology in wireless sensor networks: NP-completeness and heuristics. IEEE Trans. Mobile Comput. 2 (2003) 248–256. | DOI
[7] , and , A stop-and-start adaptive cellular genetic algorithm for mobility management of GSM-LTE cellular network users. Expert Syst. App. 106 (2018) 290–304. | DOI
[8] and , Reliable and energy efficient wireless sensor network design via conditional multi-copying for multiple central nodes. Comput. Networks 126 (2017) 57–68. | DOI
[9] and , Impact of fuel-efficient technology on automotive and fuel supply chain under government intervention: a case study. Appl. Math. Modell. 97 (2021) 771–802. | MR | Zbl | DOI
[10] and , Mathematical models for mobile network members’ coordination through coverage development-based contract. Flexible Serv. Manuf. J. (2021) 1–39.
[11] , Competition of two green and regular supply chains under environmental protection and revenue seeking policies of government. Comput. Ind. Eng. 82 (2015) 103–114. | DOI
[12] , Direct and indirect intervention schemas of government in the competition between green and non-green supply chains. J. Cleaner Prod. 170 (2018) 753–772. | DOI
[13] and , Cognitive radios for dynamic spectrum access-dynamic spectrum sharing: a game theoretical overview. IEEE Commun. Mag. 45 (2007) 88–94. | DOI
[14] , and , Inter-operator spectrum sharing for cellular networks using game theory. Paper presented at the 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications (2009). | DOI
[15] , A column generation heuristic for optimal wireless sensor network design with mobile sinks. Eur. J. Oper. Res. 260 (2017) 291–304. | MR | Zbl | DOI
[16] and , A game theoretic incentive model for closed-loop solar cell supply chain by considering government role. Energy Sources Part A (2020) 1–25.
[17] and , Assuming multiobjective metaheuristics to solve a three-objective optimisation problem for Relay Node deployment in Wireless Sensor Networks. Appl. Soft Comput. 30 (2015) 675–687. | DOI
[18] , and , Energy conservation algorithms for maintaining coverage and connectivity in wireless sensor networks. IET Commun. 4 (2010) 786–800. | MR | DOI
[19] and , Sustainable supply chains under government intervention with a real-world case study: an evolutionary game theoretic approach. Comput. Ind. Eng. 116 (2018) 130–143. | DOI
[20] , and , Fuzzy multi-objective stochastic programming model for disaster relief logistics considering telecommunication infrastructures: a case study. Oper. Res. 19 (2019) 59–99.
[21] , , , and , An efficient probabilistic routing scheme based on game theory in opportunistic networks. Comput. Networks 149 (2019) 144–153. | DOI
[22] , , , and , A novel noncooperative game competing model using generalized simple additive weighting method to perform network selection in heterogeneous wireless networks. Int. J. Commun. Syst. 28 (2015) 1112–1125. | DOI
[23] and , Impact of government financial intervention on competition among green supply chains. Int. J. Prod. Econ. 138 (2012) 201–213. | DOI
[24] , , , , , , and , Using game theory to analyze wireless ad hoc networks. IEEE Commun. Surv. Tutorials 7 (2005) 46–56. | DOI
[25] , and , Designing cellular networks using a parallel hybrid metaheuristic on the computational grid. Comput. Commun. 30 (2007) 698–713. | DOI
[26] , and , Game theory-based network selection: Solutions and challenges. IEEE Commun. Surv. Tutorials 14 (2012) 1212–1231. | DOI
[27] , and , Study on the decision-making and coordination of an e-commerce supply chain with manufacturer fairness concerns. Int. J. Prod. Res. 57 (2019) 2788–2808. | DOI
[28] and , Caching hit probability and Compressive Sensing perspective for mobile cellular networks. Simul. Modell. Prac. Theory 87 (2018) 92–98. | DOI
[29] , and , Impacts of government direct limitation on pricing, greening activities and recycling management in an online to offline closed loop supply chain. J. Cleaner Prod. 215 (2019) 1327–1340. | DOI
[30] and , Maintaining sensing coverage and connectivity in large sensor networks. Ad Hoc Sensor Wireless Networks 1 (2005) 89–124.
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