Facing the facts in cognitive radio networks that spectrum utilization cannot meet the demand of exponentially increasing communication, and artificial fish swarm algorithm maintains poor population diversity and weak global search ability, this paper propose an improved artificial fish swarm algorithm under the graph theory spectrum allocation model to obtain the optimal spectrum allocation under the network benefit function. Firstly, the field of visual and step size are adjusted adaptively to ensure the algorithm having strong global search ability of the algorithm in earlier stages and high convergence accuracy in later stages. Secondly, crazy operators are introduced into the food source to generate disturbances with increased population diversity. By comparing the total benefits of four algorithms in the same spectrum environment, and setting up control variable method separately for the available spectrum and cognitive users, we test the performance of the algorithm. Simulation experiment indicates that the improved artificial fish swarm algorithm has better global search capability and robustness compared with other three algorithms.
SU Huihui, PENG Yi, QU Wenbo
. Cognitive Radio Spectrum Allocation Based on Crazy Adaptive Fish Swarm Algorithm[J]. Journal of Applied Sciences, 2020
, 38(6)
: 882
-889
.
DOI: 10.3969/j.issn.0255-8297.2020.06.005
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