Research Article
Interference Management in Terrestrial-Satellite Networks Using Stackelberg Game
@INPROCEEDINGS{10.1007/978-3-031-23141-4_13, author={Yaomin Zhang and Haijun Zhang and Keping Long}, title={Interference Management in Terrestrial-Satellite Networks Using Stackelberg Game}, proceedings={Game Theory for Networks. 11th International EAI Conference, GameNets 2022, Virtual Event, July 7--8, 2022, Proceedings}, proceedings_a={GAMENETS}, year={2023}, month={1}, keywords={Terrestrial-satellite network Interference management Stackelberg game User fairness Cooperative scheduling}, doi={10.1007/978-3-031-23141-4_13} }- Yaomin Zhang
Haijun Zhang
Keping Long
Year: 2023
Interference Management in Terrestrial-Satellite Networks Using Stackelberg Game
GAMENETS
Springer
DOI: 10.1007/978-3-031-23141-4_13
Abstract
The terrestrial-satellite network (TSN) has been recognized a potential network realizing wide-seamless coverage and high transmission rate. In this paper, a Stackelberg game model for the interference management problem is studied in spectrum sharing TSNs, where the competition between the leaders and followers is recognized as the utility maximization between the satellite user (SU) and terrestrial users (TUs). In the leader sub-game, the utility function of SU is constructed of transmission rate and cross-tier interference reacting to the followers, and the optimal strategy is acquired by mathematical derivation. Taking account to the fairness among TUs, the maximization of the minimum worst-case transmission rate is established in the follower sub-game by optimizing the cooperative scheduling and power allocation strategy jointly. Then we propose an evolutionary gale-shapley algorithm to solve the cooperative scheduling problem. And the closed-form expression of TU power is derived by the Lagrangian dual decomposition approach. By the Stackelberg iteration of the leader strategy and follower strategy, the Stackelberg equilibrium point is ultimately obtained. We give the performance simulation to verify the convergence and effectiveness of the proposed algorithm.
