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Two-Layer Game Based Resource Allocation in Cloud Based Integrated Terrestrial-Satellite Networks
This paper investigates the cooperative transmission and resource allocation in cloud based integrated terrestrial-satellite networks, where a resource pool at the cloud acts as the integrated resource management and control center of the entire network. Considering the operator offers two levels of...
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Published in: | IEEE transactions on cognitive communications and networking 2020-06, Vol.6 (2), p.509-522 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | This paper investigates the cooperative transmission and resource allocation in cloud based integrated terrestrial-satellite networks, where a resource pool at the cloud acts as the integrated resource management and control center of the entire network. Considering the operator offers two levels of services of different quality of service (QoS) and price, we formulate a two-layer game based resource allocation problem to maximize the utility of the operator, which is composed of the Stackelberg game between the operator and users, the evolutionary game between all users, and the energy minimization problem. By solving the evolutionary game with replicator dynamics, the selections of users are obtained for any pricing strategy. Then, based on the service selections of users, the energy minimization problem is solved to allocate power and computation resources among users while satisfying the QoS constraints. By analyzing the evolution relationship between the utility and the pricing strategy, we eventually find the Stackelberg equilibrium point of the system, and obtain the optimal pricing as well as the resource allocation strategies for the operator. Finally, numerical results are provided to analyze the behavior of users in the game model, and evaluate the performance of the optimal pricing and resource allocation strategies. |
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ISSN: | 2332-7731 2332-7731 |
DOI: | 10.1109/TCCN.2020.2981016 |