Cognitive Radio Spectrum Allocation Strategy Based on Improved Genetic Algorithm
- 1 School of Electronic Engineering, Beijing University of Post & Telecommunication, Beijing China
- 2 School of Electronic Engineering, Beijing University of Post & Telecommunication, Beijing China
- 3 School of Electronic Engineering, Beijing University of Post & Telecommunication, Beijing China
- 4 School of Electronic Engineering, Beijing University of Post & Telecommunication, Beijing China
- 5 School of Electronic Engineering, Beijing University of Post & Telecommunication, Beijing China
Abstract
With the rapid development of wireless communication industry, shortage situation of spectrum resource is increasingly significant. It has become an important topic to study cognitive radio spectrum allocation algorithm that is of higher spectrum utilization ratio, less system power consumption and better algorithm efficiency. Analyzes spectrum allocation models based on genetic algorithm, and then puts forward new improved genetic algorithm. The algorithm adopts niche crowding operation to avoid individual inbreeding. It adaptively adjusts crossover and mutation probability to keep them always in the appropriate state. It provides more equal individual competition opportunity by hierarchical measures, which can effectively avert premature convergence to local optimal solution. It obviously improves the district's total transfer rate on the premise that it has met the requirements of minimum user transfer rate and limitations of maximum total power and maximum bit error rate. Simulation results prove the effectiveness of the proposed algorithm.
- P. Kolodzy, Spectrum Policy Task Force: Findings and Recommendations, International Symposium on Advanced Radio Technologies (ISART), March 2003, pp. 1-3
- X. P. Wang and L. M. Cao, “Genetic Algorithm—Theory, Application and Software Implementation,” Xi’an Press of Xi'an Jiaotong University, 2002, pp. 68-79.
- C. C. Zeng and Y. X. Zu, “Cognitive Radio Spectrum Allocation Based on Niche Adaptive Genetic Algorithm, 2011 The IET International Conference on Communication Technology and Application, Beijing, 2011, pp. 1-4.
- B. Wild and K. Ramchandran, “Detecting Primary Receivers for Cognitive Radio Applications. in Proc. IEEE Symp. New Frontiers Dynamic Spectrum Access Networks, 2005, pp. 124-130.
- R. Tandra, Fundamental Limits on Detection in Low SNR. International Conference on Wireless Networks, Communications and Mobile Computing, 2005, pp. 464-469.
- Y. X. Zu and J. Zhou, Cross-layer Resource Allocation and Packet Scheduling Scheme in Cognitive Radio Network, Invention Patent of People's Republic of China，2011, pp. 1-4.