scholarly journals A Fair Multi-Channel Assignment Algorithm With Practical Implementation in Distributed Cognitive Radio Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 14255-14267 ◽  
Author(s):  
Zong-Heng Wei ◽  
Bin-Jie Hu
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Channel Assignment ◽  
Radio Networks ◽  
Practical Implementation ◽  
Assignment Algorithm

scholarly journals ZAP: a distributed channel assignment algorithm for cognitive radio networks

2011 ◽  
Vol 2011 (1) ◽  
Author(s):  
Paulo Roberto Walenga Junior ◽  
Mauro Fonseca ◽  
Anelise Munaretto ◽  
Aline Carneiro Viana ◽  
Artur Ziviani
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Channel Assignment ◽  
Radio Networks ◽  
Assignment Algorithm

On the Use of Particle Swarm Optimization Techniques for Channel Assignments in Cognitive Radio Networks

2012 ◽  
pp. 202-214 ◽  
Author(s):  
Hisham M. Abdelsalam ◽  
Haitham S. Hamza ◽  
Abdoulraham M. Al-Shaar ◽  
Abdelbaset S. Hamza
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Channel Assignment ◽  
Assignment Problem ◽  
Pso Algorithm ◽  
Radio Networks ◽  
Optimization Techniques ◽  
Secondary Users ◽  
Swarm Optimization ◽  
Channel Assignment Problem

Efficient utilization of open spectrum in cognitive radio networks requires appropriate allocation of idle spectrum frequency bands (not used by licensed users) among coexisting cognitive radios (secondary users) while minimizing interference among all users. This problem is referred to as the spectrum allocation or the channel assignment problem in cognitive radio networks, and is shown to be NP-hard. Accordingly, different optimization techniques based on evolutionary algorithms were needed in order to solve the channel assignment problem. This chapter investigates the use of particular swarm optimization (PSO) techniques to solve the channel assignment problem in cognitive radio networks. In particular, the authors study the definitiveness of using the native PSO algorithm and the Improved Binary PSO (IBPSO) algorithm to solve the assignment problem. In addition, the performance of these algorithms is compared to that of a fine-tuned genetic algorithm (GA) for this particular problem. Three utilization functions, namely, Mean-Reward, Max-Min-Reward, and Max-Proportional-Fair, are used to evaluate the effectiveness of three optimization algorithms. Extensive simulation results show that PSO and IBPSO algorithms outperform that fine-tuned GA. More interestingly, the native PSO algorithm outperforms both the GA and the IBPSO algorithms in terms of solution speed and quality.

Channel assignment based on bee algorithms in multi-hop cognitive radio networks

2014 ◽  
Vol 8 (13) ◽  
pp. 2356-2365 ◽  
Author(s):  
Ahmad Ghasemi ◽  
Foad Qassemi ◽  
M. Biguesh ◽  
Mohammad Ali Masnadi-Shirazi
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Channel Assignment ◽  
Radio Networks

Spectrum Assignment Algorithm Based on Clonal Selection in Cognitive Radio Networks

2012 ◽  
Vol 457-458 ◽  
pp. 931-939
Author(s):  
Xiao Fei Wang ◽  
Xi Zhang ◽  
Yue Bing Chen ◽  
Lei Zhang ◽  
Chao Jing Tang
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Constraint Satisfaction ◽  
Clonal Selection ◽  
Radio Networks ◽  
Performance Comparison ◽  
Immune Optimization ◽  
Spectrum Assignment ◽  
Network Utilization ◽  
Assignment Algorithm

An improved-immune-clonal-selection based spectrum assignment algorithm (IICSA) in cognitive radio networks is proposed, combing graph theory and immune optimization. It uses constraint satisfaction operation to make encoded antibody population satisfy constraints, and realizes the global optimization. The random-constraint satisfaction operator and fair-constraint satisfaction operator are designed to guarantee efficiency and fairness, respectively. Simulations are performed for performance comparison between the IICSA and the color-sensitive graph coloring algorithm. The results indicate that the proposed algorithm increases network utilization, and efficiently improves the fairness.

Sniffer Channel Assignment With Imperfect Monitoring for Cognitive Radio Networks

2016 ◽  
Vol 15 (3) ◽  
pp. 1703-1715 ◽  
Author(s):  
Jing Xu ◽  
Qingsi Wang ◽  
Kai Zeng ◽  
Mingyan Liu ◽  
Wei Liu
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Channel Assignment ◽  
Radio Networks ◽  
Imperfect Monitoring
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