scholarly journals A Dynamic Spectrum Allocation Algorithm for a Maritime Cognitive Radio Communication System Based on a Queuing Model

Information ◽  
2017 ◽  
Vol 8 (4) ◽  
pp. 119 ◽  
Author(s):  
Jingbo Zhang ◽  
Jianyu Yang ◽  
Yiying Zhang ◽  
Shufang Zhang
Keyword(s):  
Cognitive Radio ◽  
Communication System ◽  
Dynamic Spectrum ◽  
Spectrum Allocation ◽  
Dynamic Spectrum Allocation ◽  
Radio Communication ◽  
Queuing Model ◽  
Allocation Algorithm ◽  
Radio Communication System

Research on Dynamic Spectrum Allocation Using Cognitive Radio Technologies

2012 ◽  
Vol 35 (3) ◽  
pp. 446-453 ◽  
Author(s):  
Yu-Ming GE ◽  
Yi SUN ◽  
Hai JIANG ◽  
Jun LI ◽  
Zhong-Cheng LI
Keyword(s):  
Cognitive Radio ◽  
Dynamic Spectrum ◽  
Spectrum Allocation ◽  
Dynamic Spectrum Allocation

scholarly journals Spectrum Allocation in Cognitive Radio: A PSO-based Approach

2019 ◽  
Vol 63 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Subhashree Mishra ◽  
Santwana Sagnika ◽  
Sudhansu Sekhar Singh ◽  
Bhabani Shankar Prasad Mishra
Keyword(s):  
Cognitive Radio ◽  
Power Consumption ◽  
Transfer Rate ◽  
Optimal Solution ◽  
Maximum Error ◽  
Dynamic Spectrum ◽  
Spectrum Allocation ◽  
Dynamic Spectrum Allocation ◽  
Rate Maximum ◽  
Radio Systems

Cognitive radio systems have taken a fore-running position in the wireless communication technology. With most of the communication taking place through multi-carrier systems, the allocation of available spectrum to various carriers is a prominent issue. Since cognitive systems provide an environment of dynamic spectrum allocation, it becomes necessary to perform dynamic spectrum allocation swiftly with due consideration of parameters, like power consumption, fair distribution and minimal error. This paper considers a Particle Swarm Optimization-based approach, popularly used for solving large problems involving complex solution spaces to reach an optimal solution within feasible time. The mentioned spectrum allocation problem has been solved using PSO with a view to maximize the total transfer rate of the system, within specified constraints of maximum error rate, maximum power consumption and minimum transfer rate per user. The results have been compared with the existing Genetic Algorithm-based approach and have proved to be more effective.

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