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

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.

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

scholarly journals A Survey on Security Aware Channel Assignment in Cognitive Networks (CRNs)

2020 ◽  
pp. 245-247
Keyword(s):  
Cognitive Radio ◽  
Channel State Information ◽  
Cognitive Radio Networks ◽  
Channel Assignment ◽  
High Accuracy ◽  
Radio Networks ◽  
Cognitive Networks ◽  
Cognitive Systems ◽  
Channel State ◽  
State Information

This paper presents a survey on Security Aware Channel Assignment for Cognitive Radio Networks (CRNs). Cognitive networks are networks are the sort of networks which show the attributes of leveraging the channel state information for the utilization of resources such as bandwidth and energy. The major challenge with cognitive systems comprising of cognitive networks is the fact that finding the channel state information with high accuracy is often extremely complex in nature. Moreover, securing such a network against attacks is even more challenging. This paper focuses on contemporary contributions in the field.

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