Power Allocation Based on Genetic Simulated Annealing Algorithm in Cognitive Radio Networks

ZHAO Junhui; GUAN Xin; LI Xiuping

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ZHAO Junhui, GUAN Xin, LI Xiuping. Power Allocation Based on Genetic Simulated Annealing Algorithm in Cognitive Radio Networks[J]. Chinese Journal of Electronics, 2013, 22(1): 177-180.

Citation:

ZHAO Junhui, GUAN Xin, LI Xiuping. Power Allocation Based on Genetic Simulated Annealing Algorithm in Cognitive Radio Networks[J]. Chinese Journal of Electronics, 2013, 22(1): 177-180.

ZHAO Junhui, GUAN Xin, LI Xiuping. Power Allocation Based on Genetic Simulated Annealing Algorithm in Cognitive Radio Networks[J]. Chinese Journal of Electronics, 2013, 22(1): 177-180.

Citation:

ZHAO Junhui, GUAN Xin, LI Xiuping. Power Allocation Based on Genetic Simulated Annealing Algorithm in Cognitive Radio Networks[J]. Chinese Journal of Electronics, 2013, 22(1): 177-180.

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Power Allocation Based on Genetic Simulated Annealing Algorithm in Cognitive Radio Networks

1.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;
2.
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;
3.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61172073), the State Key Laboratory of Rail Traffic Control and Safety (No.RCS2011ZT003), Beijing Jiaotong University, the open research fund of National Mobile Communications Research Laboratory, Southeast University, China, and the Fundamental Research Funds for the Central Universities, Beijing Jiaotong University.

Received Date: 2011-12-01
Rev Recd Date: 2012-05-01
Publish Date: 2013-01-05

Abstract

Abstract

To maximize the utility of cognitive networks, the interference constraints to ensure primary users’ Quality of service (QoS) standards is considered in this paper. The interference temperature model was used to model network interference effects, so that the interference caused by cognitive user’s signal transmission cannot surpass the primary user’s interference temperature limitation. Multi-users’ power allocation was studied based on the analysis of multiple interference temperature limitation of the power model and multi-user access power control. The power allocation issue is converted into a multiconstrained nonlinear programming problem with the interference temperature limitation and then an improved simulated annealing genetic algorithm is proposed to solve this problem. Simulation results show that the improved simulated annealing genetic algorithm has better accuracy and convergence performance.
- Cognitive radio network,
- Interference temperature,
- Power allocation,
- Genetic simulated annealing algorithm.

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References(10)

References

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