scholarly journals Joint Voronoi diagram and game theory-based power control scheme for the HetNet small cell networks

2014 ◽  
Vol 2014 (1) ◽  
Author(s):  
Xiaodong Xu ◽  
Yi Li ◽  
Rui Gao ◽  
Xiaofeng Tao
Keyword(s):  
Game Theory ◽  
Power Control ◽  
Voronoi Diagram ◽  
Small Cell ◽  
Small Cell Networks ◽  
Control Scheme ◽  
Cell Networks

Game Theory-Based Coverage Optimization for Small Cell Networks

2015 ◽  
pp. 184-211
Author(s):  
Yiqing Zhou ◽  
Liang Huang ◽  
Lin Tian ◽  
Jinglin Shi
Keyword(s):  
Game Theory ◽  
Power Control ◽  
Optimization Methods ◽  
Small Cell ◽  
Small Cell Networks ◽  
Control Center ◽  
Coverage Optimization ◽  
Control Scheme ◽  
Coverage Problems ◽  
Cell Networks

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-Based Coverage Optimization for Small Cell Networks

Game Theory ◽  
2017 ◽  
pp. 177-203
Author(s):  
Yiqing Zhou ◽  
Liang Huang ◽  
Lin Tian ◽  
Jinglin Shi
Keyword(s):  
Game Theory ◽  
Power Control ◽  
Optimization Methods ◽  
Small Cell ◽  
Small Cell Networks ◽  
Control Center ◽  
Coverage Optimization ◽  
Control Scheme ◽  
Coverage Problems ◽  
Cell Networks

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.

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
Keyword(s):  
Power Control ◽  
Optimization Problems ◽  
Stackelberg Game ◽  
Small Cell ◽  
User Equipment ◽  
Stackelberg Equilibrium ◽  
Small Cell Networks ◽  
Transmit Power ◽  
Control Scheme ◽  
Cell Networks

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.

Game Theory-Based Radio Resource Optimization in Heterogeneous Small Cell Networks (HSCNs)

2015 ◽  
pp. 137-183
Author(s):  
Mugen Peng ◽  
Yaohua Sun ◽  
Chengdan Sun ◽  
Manzoor Ahmed
Keyword(s):  
Game Theory ◽  
Resource Allocation ◽  
Small Cell ◽  
Base Stations ◽  
Radio Resource Allocation ◽  
Radio Resource ◽  
Small Cell Networks ◽  
The Game Theory ◽  
Simulation Results ◽  
Cell Networks

To optimize radio resource allocation, the game theory is utilized as a powerful tool because its characteristic can be adaptive to the distribution characteristics of in heterogeneous small cell networks (HSCNs). This chapter summarizes the recent achievements for the game theory based radio resource allocation in HSCNs, where macro base stations (MBSs) and dense small cell base stations (SBSs) share the same frequency spectrum and interfere with each other. Two kinds of game models are introduced to optimize the radio resource allocation, namely the non-cooperative Stackelberg and the cooperative coalition. System models, optimization problem formulation, problem solution, and simulation results for these two kinds of game models are presented. Particularly, the Stackelberg models for HSCNs are presented with the Stackelberg equilibrium and the closed-form expressions. The coalition formations for traditional HCSNs, cloud small cell networks, and heterogeneous cloud small cell networks are introduced. Simulation results are shown to demonstrate the proposed game theory based radio resource optimization strategies converged and efficient.

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