Abstract | ||
---|---|---|
Geometry-based optimal power control was proposed in [14] to transform the power-control problem to a new geometrical problem on the position relationship between a line and some points. This scheme provides a novel visual perspective and lowers the complexity of optimization. We generalize this scheme to a larger class of power-control optimization problems so as to maximize the network utility with multiple average and peak power constraints in wireless networks. To facilitate the handling of the geometrical model, we define a subset of geometrical models with specified characteristics, called a regular geometrical model, and derive the type of power-control problems eligible for the regular geometrical model. For such a type of problems, two strategies are proposed for the construction of the regular geometrical model. Utilizing geometrical properties, we propose a novel geometry-based optimization scheme for the general power-control problem. Its computational complexity is significantly lower than the conventional algorithms. We also provide a further discussion on irregular geometrical model cases. Finally, we provide two examples of deploying the proposed geometry-based power-control scheme. © 2012 IEEE. |
Year | DOI | Venue |
---|---|---|
2012 | 10.1109/SECON.2012.6275835 | SECON |
Keywords | Field | DocType |
silicon,visual perspective,computational modeling,optimization,computational complexity,optimal control,interference,geometry,power control,wireless networks | Wireless network,Mathematical optimization,Optimal control,Computer science,Power control,Network utility,Interference (wave propagation),Geometry,Optimization problem,Computational resource,Computational complexity theory | Conference |
Volume | Issue | ISSN |
1 | null | 21555494 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Wei Wang | 1 | 477 | 53.58 |
Kang G. Shin | 2 | 14055 | 1487.46 |
Z. Zhang | 3 | 2308 | 198.54 |
Wang Wenbo | 4 | 1200 | 130.70 |
Tao Peng | 5 | 391 | 46.51 |