scholarly journals Power Control via Stackelberg Game for Small-Cell Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Yanxiang Jiang ◽  
Hui Ge ◽  
Mehdi Bennis ◽  
Fu-Chun Zheng ◽  
Xiaohu You

In this paper, power control in the uplink for two-tier small-cell networks is investigated. We formulate the power control problem as a Stackelberg game, where the macrocell user equipment (MUE) acts as the leader and the small-cell user equipment (SUE) acts as the follower. To reduce the cross-tier and cotier interferences and the power consumption of both the MUE and SUE, we propose optimizing not only the transmit rate but also the transmit power. The corresponding optimization problems are solved through a two-layer iteration. In the inner iteration, the SUE items (SUEs) compete with each other, and their optimal transmit powers are obtained through iterative computations. In the outer iteration, the optimal transmit power of the MUE is obtained in a closed form based on the transmit powers of the SUEs through proper mathematical manipulations. We prove the convergence of the proposed power control scheme, and we also theoretically show the existence and uniqueness of the Stackelberg equilibrium (SE) in the formulated Stackelberg game. The simulation results show that the proposed power control scheme provides considerable improvements, particularly for the MUE.

Author(s):  
Yiqing Zhou ◽  
Liang Huang ◽  
Lin Tian ◽  
Jinglin Shi

Focusing on the coverage optimization of small cell networks (SCN), this chapter starts with a detailed analysis on various coverage problems, based on which the coverage optimization problem is formulated. Then centralized and distributed coverage optimization methods based on game theory are described. Firstly, considering the coverage optimization with a control center, a modified particle swarm optimization (MPSO) is presented for the self-optimization of SCN, which employs a heuristic power control scheme to search for the global optimum solution. Secondly, distributed optimization using game theory (DGT) without a control center is concerned. Considering both throughput and interference, a utility function is formulated. Then a power control scheme is proposed to find the Nash Equilibrium (NE). Simulation results show that MPSO and DGT significantly outperform conventional schemes. Moreover, compared with MPSO, DGT uses much less overhead. Finally, further research directions are discussed and conclusions are drawn.


Game Theory ◽  
2017 ◽  
pp. 177-203
Author(s):  
Yiqing Zhou ◽  
Liang Huang ◽  
Lin Tian ◽  
Jinglin Shi

Focusing on the coverage optimization of small cell networks (SCN), this chapter starts with a detailed analysis on various coverage problems, based on which the coverage optimization problem is formulated. Then centralized and distributed coverage optimization methods based on game theory are described. Firstly, considering the coverage optimization with a control center, a modified particle swarm optimization (MPSO) is presented for the self-optimization of SCN, which employs a heuristic power control scheme to search for the global optimum solution. Secondly, distributed optimization using game theory (DGT) without a control center is concerned. Considering both throughput and interference, a utility function is formulated. Then a power control scheme is proposed to find the Nash Equilibrium (NE). Simulation results show that MPSO and DGT significantly outperform conventional schemes. Moreover, compared with MPSO, DGT uses much less overhead. Finally, further research directions are discussed and conclusions are drawn.


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